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Ming Z, Yu W, Fan J, Ling G, Fengming C, Wei T. Efficacy of kinesthetic motor imagery based brain computer interface combined with tDCS on upper limb function in subacute stroke. Sci Rep 2025; 15:11829. [PMID: 40195429 PMCID: PMC11977199 DOI: 10.1038/s41598-025-96039-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2024] [Accepted: 03/25/2025] [Indexed: 04/09/2025] Open
Abstract
This study investigates whether the combined effect of kinesthetic motor imagery-based brain computer interface (KI-BCI) and transcranial direct current stimulation (tDCS) on upper limb function in subacute stroke patients is more effective than using KI-BCI or tDCS alone. Forty-eight subacute stroke survivors were randomized to the KI-BCI, tDCS, or BCI-tDCS group. The KI-BCI group performed 30 min of KI-BCI training. Patients in tDCS group received 30 min of tDCS. Patients in BCI-tDCS group received 15 min of tDCS and 15 min of KI-BCI. The treatment cycle was five times a week, for four weeks. After all intervention, the Fugl-Meyer Assessment-Upper Extremity, Motor Status Scale, and the Modified Barthel Index scores of the KI-BCI group were superior to those of the tDCS group. The BCI-tDCS group was superior to the tDCS group in terms of the Motor Status Scale. Although quantitative EEG showed no significant group differences, the quantitative EEG indices in the tDCS group were significantly lower than before treatment. In conclusion, after treatment, although all intervention strategies improved upper limb motor function and daily living abilities in subacute stroke patients, KI-BCI demonstrated significantly better efficacy than tDCS. Under the same total treatment duration, the combined use of tDCS and KI-BCI did not achieve the hypothesized optimal outcome. Notably, tDCS reduced QEEG indices, possibly indicating favorable future outcomes in future.Trial registry number: ChiCTR2000034730.
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Affiliation(s)
- Zhang Ming
- Department of Mechatronic Engineering, China University of Mining and Technology, Jiangsu, China
- The Affiliated Xuzhou Rehabilitation Hospital of Xuzhou Medical University, Xuzhou Medical University, Jiangsu, China
| | - Wu Yu
- Department of Sports Science, Zhejiang University, Hangzhou, China
| | - Jia Fan
- Department of Rehabilitation, Chongming Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Gao Ling
- The Affiliated Xuzhou Rehabilitation Hospital of Xuzhou Medical University, Xuzhou Medical University, Jiangsu, China
| | - Chu Fengming
- The Affiliated Xuzhou Rehabilitation Hospital of Xuzhou Medical University, Xuzhou Medical University, Jiangsu, China
| | - Tang Wei
- Department of Mechatronic Engineering, China University of Mining and Technology, Jiangsu, China.
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Marissens Cueva V, Bougrain L, Lotte F, Rimbert S. Reliable predictor of BCI motor imagery performance using median nerve stimulation. J Neural Eng 2025; 22:026039. [PMID: 40127541 DOI: 10.1088/1741-2552/adc48d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2024] [Accepted: 03/24/2025] [Indexed: 03/26/2025]
Abstract
Objective.Predicting performance in brain-computer interfaces (BCIs) is crucial for enhancing user experience, optimizing training and identifying the most efficient BCI approach for each individual.Approach.This study explores the use of median nerve stimulation (MNS) as a predictor of motor imagery (MI)-BCI performance. MNS induces event related (de)synchronization (ERD/ERS) patterns in the brain that are similar to those generated during MI tasks, providing a non-invasive, user-independent, and easy-to-setup method for performance prediction.Main results.Our proposed predictor, based on the minimum value of the ERD induced by the MNS, not only exhibits a robust correlation with the MI-BCI performance accuracy (rho = -0.71,p<0.001), but also effectively predicts this performance with a significant correlation (rho = 0.61, mean absolute error = 9.0,p<0.01). These results demonstrate its validity as a reliable predictor of MI-BCI performance.Significance.By systematically analyzing patterns induced by MNS and correlating them with subsequent MI-BCI task performance, we aim to establish a robust predictive method of motor activity to each individual only based on MNS, making it possible, among other things, to passively predict BCI deficiency or proficiency, and to potentially adapt BCI parameters for an efficient BCI experience or BCI-based recovery.
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Affiliation(s)
- Valérie Marissens Cueva
- Inria Center at the University of Bordeaux / LaBRI, Talence, France
- Université de Lorraine, CNRS, LORIA, F-54000 Nancy, France
| | | | - Fabien Lotte
- Inria Center at the University of Bordeaux / LaBRI, Talence, France
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Bhadra K, Giraud AL, Marchesotti S. Learning to operate an imagined speech Brain-Computer Interface involves the spatial and frequency tuning of neural activity. Commun Biol 2025; 8:271. [PMID: 39979463 PMCID: PMC11842755 DOI: 10.1038/s42003-025-07464-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 01/03/2025] [Indexed: 02/22/2025] Open
Abstract
Brain-Computer Interfaces (BCI) will revolutionize the way people with severe impairment of speech production can communicate. While current efforts focus on training classifiers on vast amounts of neurophysiological signals to decode imagined speech, much less attention has been given to users' ability to adapt their neural activity to improve BCI-control. To address whether BCI-control improves with training and characterize the underlying neural dynamics, we trained 15 healthy participants to operate a binary BCI system based on electroencephalography (EEG) signals through syllable imagery for five consecutive days. Despite considerable interindividual variability in performance and learning, a significant improvement in BCI-control was globally observed. Using a control experiment, we show that a continuous feedback about the decoded activity is necessary for learning to occur. Performance improvement was associated with a broad EEG power increase in frontal theta activity and focal enhancement in temporal low-gamma activity, showing that learning to operate an imagined-speech BCI involves dynamic changes in neural features at different spectral scales. These findings demonstrate that combining machine and human learning is a successful strategy to enhance BCI controllability.
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Affiliation(s)
- Kinkini Bhadra
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Anne-Lise Giraud
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Université Paris Cité, Institut Pasteur, AP-HP, Inserm, Fondation Pour l'Audition, Institut de l'Audition, IHU reConnect, Paris, France
| | - Silvia Marchesotti
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
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Fló E, Fraiman D, Sitt JD. Assessing brain-muscle networks during motor imagery to detect covert command-following. BMC Med 2025; 23:68. [PMID: 39915775 PMCID: PMC11803995 DOI: 10.1186/s12916-025-03846-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 01/06/2025] [Indexed: 02/09/2025] Open
Abstract
BACKGROUND In this study, we evaluated the potential of a network approach to electromyography and electroencephalography recordings to detect covert command-following in healthy participants. The motivation underlying this study was the development of a diagnostic tool that can be applied in common clinical settings to detect awareness in patients that are unable to convey explicit motor or verbal responses, such as patients that suffer from disorders of consciousness (DoC). METHODS We examined the brain and muscle response during movement and imagined movement of simple motor tasks, as well as during resting state. Brain-muscle networks were obtained using non-negative matrix factorization (NMF) of the coherence spectra for all the channel pairs. For the 15/38 participants who showed motor imagery, as indexed by common spatial filters and linear discriminant analysis, we contrasted the configuration of the networks during imagined movement and resting state at the group level, and subject-level classifiers were implemented using as features the weights of the NMF together with trial-wise power modulations and heart response to classify resting state from motor imagery. RESULTS Kinesthetic motor imagery produced decreases in the mu-beta band compared to resting state, and a small correlation was found between mu-beta power and the kinesthetic imagery scores of the Movement Imagery Questionnaire-Revised Second version. The full-feature classifiers successfully distinguished between motor imagery and resting state for all participants, and brain-muscle functional networks did not contribute to the overall classification. Nevertheless, heart activity and cortical power were crucial to detect when a participant was mentally rehearsing a movement. CONCLUSIONS Our work highlights the importance of combining EEG and peripheral measurements to detect command-following, which could be important for improving the detection of covert responses consistent with volition in unresponsive patients.
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Affiliation(s)
- Emilia Fló
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, INSERM, CNRS, Paris, France.
| | - Daniel Fraiman
- Departamento de Matemática y Ciencias, Universidad de San Andrés, Buenos Aires, Argentina
- CONICET, Buenos Aires, Argentina
| | - Jacobo Diego Sitt
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, INSERM, CNRS, Paris, France.
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Kusano K, Hayashi M, Iwama S, Ushiba J. Improved motor imagery skills after repetitive passive somatosensory stimulation: a parallel-group, pre-registered study. Front Neural Circuits 2025; 18:1510324. [PMID: 39839676 PMCID: PMC11747441 DOI: 10.3389/fncir.2024.1510324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2024] [Accepted: 12/18/2024] [Indexed: 01/23/2025] Open
Abstract
Introduction Motor-imagery-based Brain-Machine Interface (MI-BMI) has been established as an effective treatment for post-stroke hemiplegia. However, the need for long-term intervention can represent a significant burden on patients. Here, we demonstrate that motor imagery (MI) instructions for BMI training, when supplemented with somatosensory stimulation in addition to conventional verbal instructions, can help enhance MI capabilities of healthy participants. Methods Sixteen participants performed MI during scalp EEG signal acquisition before and after somatosensory stimulation to assess MI-induced cortical excitability, as measured using the event-related desynchronization (ERD) of the sensorimotor rhythm (SMR). The non-dominant left hand was subjected to neuromuscular electrical stimulation above the sensory threshold but below the motor threshold (St-NMES), along with passive movement stimulation using an exoskeleton. Participants were randomly divided into an intervention group, which received somatosensory stimulation, and a control group, which remained at rest without stimulation. Results The intervention group exhibited a significant increase in SMR-ERD compared to the control group, indicating that somatosensory stimulation contributed to improving MI ability. Discussion This study demonstrates that somatosensory stimulation, combining electrical and mechanical stimuli, can improve MI capability and enhance the excitability of the sensorimotor cortex in healthy individuals.
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Affiliation(s)
- Kyoko Kusano
- Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, Kanagawa, Japan
| | - Masaaki Hayashi
- Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, Kanagawa, Japan
- LIFESCAPES Inc., Tokyo, Japan
| | - Seitaro Iwama
- Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, Kanagawa, Japan
| | - Junichi Ushiba
- Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, Kanagawa, Japan
- LIFESCAPES Inc., Tokyo, Japan
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Estradera-Bel M, La Touche R, Pro-Marín D, Cuenca-Martínez F, Paris-Alemany A, Grande-Alonso M. Exploring temporal congruence in motor imagery and movement execution in non-specific chronic low back pain. Brain Cogn 2024; 182:106227. [PMID: 39454412 DOI: 10.1016/j.bandc.2024.106227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Revised: 09/24/2024] [Accepted: 10/17/2024] [Indexed: 10/28/2024]
Abstract
Chronic non-specific low back pain (NSCLBP) is linked to sensorimotor dysfunctions and altered motor planning, likely due to neuroplastic changes. Motor imagery (MI) and movement execution share neural pathways, but the relationship between imagined and executed movements in NSCLBP patients remains underexplored. This study aimed to assess the temporal congruence between imagined and executed movements in NSCLBP sufferers, with secondary goals of investigating group differences in movement chronometry, psychological well-being, and disability, as well as possible correlations among these factors. Fifty-six participants, including 28 NSCLBP patients and 28 asymptomatic subjects (AS), performed lumbar flexion and Timed Up and Go (TUG) tasks. NSCLBP patients showed significant temporal incongruence in both tasks, executing movements more slowly than imagined, whereas AS displayed incongruence only in the TUG task. NSCLBP patients also took longer to imagine and execute lumbar flexion movements compared to AS, with correlations observed between execution delays, higher disability, and greater fear of movement. The findings highlight a lack of temporal congruence in NSCLBP patients, especially in lumbar flexion, emphasizing the complex relationship between chronic pain, motor ability, and psychological factors. These results suggest that integrated treatment approaches addressing cognitive and emotional aspects are crucial for managing NSCLBP.
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Affiliation(s)
- Manuel Estradera-Bel
- Unidad de Trastornos Musculoesqueléticos, Instituto de Rehabilitación Funcional (IRF) La Salle, Centro Superior Estudios Universitarios (CSEU) La Salle, Universidad Autónoma de Madrid, Madrid, Spain
| | - Roy La Touche
- Motion in Brains Research Group, Centro Superior de Estudios Universitarios (CSEU) La Salle, Universidad Autónoma de Madrid, Madrid, Spain; Instituto de Dolor Craneofacial y Neuromusculoesquelético (INDCRAN), Madrid, Spain; Departamento de Fisioterapia, Centro Superior de Estudios Universitarios (CSEU) La Salle, Universidad Autónoma de Madrid, Madrid, Spain
| | - Diego Pro-Marín
- Unidad de Trastornos Musculoesqueléticos, Instituto de Rehabilitación Funcional (IRF) La Salle, Centro Superior Estudios Universitarios (CSEU) La Salle, Universidad Autónoma de Madrid, Madrid, Spain
| | - Ferran Cuenca-Martínez
- Department of Physiotherapy, University of Valencia, Gascó Oliag n° 5, Valencia 46010, Spain
| | - Alba Paris-Alemany
- Motion in Brains Research Group, Centro Superior de Estudios Universitarios (CSEU) La Salle, Universidad Autónoma de Madrid, Madrid, Spain; Instituto de Dolor Craneofacial y Neuromusculoesquelético (INDCRAN), Madrid, Spain; Departamento de Radiología, Rehabilitación y Fisioterapia. Facultad de Enfermería, Fisioterapia y Podología. Universidad Complutense de Madrid, Madrid, Spain.
| | - Mónica Grande-Alonso
- Universidad de Alcalá, Facultad de Medicina, Departamento de Cirugía, Ciencias Médicas y Sociales, Alcalá de Henares, Spain
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Martinez-Peon D, Garcia-Hernandez NV, Benavides-Bravo FG, Parra-Vega V. Characterization and classification of kinesthetic motor imagery levels. J Neural Eng 2024; 21:046024. [PMID: 38963179 DOI: 10.1088/1741-2552/ad5f27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 06/27/2024] [Indexed: 07/05/2024]
Abstract
Objective.Kinesthetic Motor Imagery (KMI) represents a robust brain paradigm intended for electroencephalography (EEG)-based commands in brain-computer interfaces (BCIs). However, ensuring high accuracy in multi-command execution remains challenging, with data from C3 and C4 electrodes reaching up to 92% accuracy. This paper aims to characterize and classify EEG-based KMI of multilevel muscle contraction without relying on primary motor cortex signals.Approach.A new method based on Hurst exponents is introduced to characterize EEG signals of multilevel KMI of muscle contraction from electrodes placed on the premotor, dorsolateral prefrontal, and inferior parietal cortices. EEG signals were recorded during a hand-grip task at four levels of muscle contraction (0%, 10%, 40%, and 70% of the maximal isometric voluntary contraction). The task was executed under two conditions: first, physically, to train subjects in achieving muscle contraction at each level, followed by mental imagery under the KMI paradigm for each contraction level. EMG signals were recorded in both conditions to correlate muscle contraction execution, whether correct or null accurately. Independent component analysis (ICA) maps EEG signals from the sensor to the source space for preprocessing. For characterization, three algorithms based on Hurst exponents were used: the original (HO), using partitions (HRS), and applying semivariogram (HV). Finally, seven classifiers were used: Bayes network (BN), naive Bayes (NB), support vector machine (SVM), random forest (RF), random tree (RT), multilayer perceptron (MP), and k-nearest neighbors (kNN).Main results.A combination of the three Hurst characterization algorithms produced the highest average accuracy of 96.42% from kNN, followed by MP (92.85%), SVM (92.85%), NB (91.07%), RF (91.07%), BN (91.07%), and RT (80.35%). of 96.42% for kNN.Significance.Results show the feasibility of KMI multilevel muscle contraction detection and, thus, the viability of non-binary EEG-based BCI applications without using signals from the motor cortex.
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Affiliation(s)
- D Martinez-Peon
- Department of Electrical and Electronic Engineering, National Technological Institute of Mexico (TecNM)- IT Nuevo Leon, Guadalupe, Mexico
| | - N V Garcia-Hernandez
- National Council on Science and Technology, Saltillo, Mexico
- Robotics and Advanced Manufacturing, Research Center for Advanced Studies (Cinvestav), Saltillo, Mexico
| | - F G Benavides-Bravo
- Department of Basic Sciences, National Technological Institute of Mexico (TecNM)- IT Nuevo Leon, Guadalupe, Mexico
| | - V Parra-Vega
- Robotics and Advanced Manufacturing, Research Center for Advanced Studies (Cinvestav), Saltillo, Mexico
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Zapała D, Augustynowicz P, Tokovarov M, Iwanowicz P, Droździel P. Brief Visual Deprivation Effects on Brain Oscillations During Kinesthetic and Visual-motor Imagery. Neuroscience 2023; 532:37-49. [PMID: 37625688 DOI: 10.1016/j.neuroscience.2023.08.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 08/10/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023]
Abstract
It is widely recognized that opening and closing the eyes can direct attention to external or internal stimuli processing. This has been confirmed by studies showing the effects of changes in visual stimulation changes on cerebral activity during different tasks, e.g., motor imagery and execution. However, an essential aspect of creating a mental representation of motion, such as imagery perspective, has not yet been investigated in the present context. Our study aimed to verify the effect of brief visual deprivation (under eyes open [EO] and eyes closed [EC] conditions) on brain wave oscillations and behavioral performance during kinesthetic imagery (KMI) and visual-motor imagery (VMI) tasks. We focused on the alpha and beta rhythms from visual- and motor-related EEG activity sources. Additionally, we used machine learning algorithms to establish whether the registered differences in brain oscillations might affect motor imagery brain-computer interface (MI-BCI) performance. The results showed that the occipital areas in the EC condition presented significantly stronger desynchronization during VMI tasks, which is typical for enhanced visual stimuli processing. Furthermore, the stronger desynchronization of alpha rhythms from motor areas in the EO, than EC condition confirmed previous effects obtained during real movements. It was also found that simulating movement under EC/EO conditions affected signal classification accuracy, which has practical implications for MI-BCI effectiveness. These findings suggest that shifting processing toward external or internal stimuli modulates brain rhythm oscillations associated with different perspectives on the mental representation of movement.
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Affiliation(s)
- Dariusz Zapała
- Institute of Psychology, Department of Experimental Psychology, The John Paul II Catholic University of Lublin, 20950 Lublin, Poland.
| | - Paweł Augustynowicz
- Institute of Psychology, Department of Experimental Psychology, The John Paul II Catholic University of Lublin, 20950 Lublin, Poland.
| | | | - Paulina Iwanowicz
- Institute of Psychology, Department of Experimental Psychology, The John Paul II Catholic University of Lublin, 20950 Lublin, Poland.
| | - Paulina Droździel
- Institute of Psychology, Department of Experimental Psychology, The John Paul II Catholic University of Lublin, 20950 Lublin, Poland.
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Mang J, Xu Z, Qi Y, Zhang T. Favoring the cognitive-motor process in the closed-loop of BCI mediated post stroke motor function recovery: challenges and approaches. Front Neurorobot 2023; 17:1271967. [PMID: 37881517 PMCID: PMC10595019 DOI: 10.3389/fnbot.2023.1271967] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 09/08/2023] [Indexed: 10/27/2023] Open
Abstract
The brain-computer interface (BCI)-mediated rehabilitation is emerging as a solution to restore motor skills in paretic patients after stroke. In the human brain, cortical motor neurons not only fire when actions are carried out but are also activated in a wired manner through many cognitive processes related to movement such as imagining, perceiving, and observing the actions. Moreover, the recruitment of motor cortexes can usually be regulated by environmental conditions, forming a closed-loop through neurofeedback. However, this cognitive-motor control loop is often interrupted by the impairment of stroke. The requirement to bridge the stroke-induced gap in the motor control loop is promoting the evolution of the BCI-based motor rehabilitation system and, notably posing many challenges regarding the disease-specific process of post stroke motor function recovery. This review aimed to map the current literature surrounding the new progress in BCI-mediated post stroke motor function recovery involved with cognitive aspect, particularly in how it refired and rewired the neural circuit of motor control through motor learning along with the BCI-centric closed-loop.
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Affiliation(s)
- Jing Mang
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Zhuo Xu
- Department of Rehabilitation, China-Japan Union Hospital of Jilin University, Changchun, China
| | - YingBin Qi
- Department of Neurology, Jilin Province People's Hospital, Changchun, China
| | - Ting Zhang
- Rehabilitation Therapeutics, School of Nursing, Jilin University, Changchun, China
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Wang W, Shi B, Wang D, Wang J, Liu G. Enhanced lower-limb motor imagery by kinesthetic illusion. Front Neurosci 2023; 17:1077479. [PMID: 37409102 PMCID: PMC10319417 DOI: 10.3389/fnins.2023.1077479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 05/30/2023] [Indexed: 07/07/2023] Open
Abstract
Brain-computer interface (BCI) based on lower-limb motor imagery (LMI) enables hemiplegic patients to stand and walk independently. However, LMI ability is usually poor for BCI-illiterate (e.g., some stroke patients), limiting BCI performance. This study proposed a novel LMI-BCI paradigm with kinesthetic illusion(KI) induced by vibratory stimulation on Achilles tendon to enhance LMI ability. Sixteen healthy subjects were recruited to carry out two research contents: (1) To verify the feasibility of induced KI by vibrating Achilles tendon and analyze the EEG features produced by KI, research 1 compared the subjective feeling and brain activity of participants during rest task with and without vibratory stimulation (V-rest, rest). (2) Research 2 compared the LMI-BCI performance with and without KI (KI-LMI, no-LMI) to explore whether KI enhances LMI ability. The analysis methods of both experiments included classification accuracy (V-rest vs. rest, no-LMI vs. rest, KI-LMI vs. rest, KI-LMI vs. V-rest), time-domain features, oral questionnaire, statistic analysis and brain functional connectivity analysis. Research 1 verified that induced KI by vibrating Achilles tendon might be feasible, and provided a theoretical basis for applying KI to LMI-BCI paradigm, evidenced by oral questionnaire (Q1) and the independent effect of vibratory stimulation during rest task. The results of research 2 that KI enhanced mesial cortex activation and induced more intensive EEG features, evidenced by ERD power, topographical distribution, oral questionnaire (Q2 and Q3), and brain functional connectivity map. Additionally, the KI increased the offline accuracy of no-LMI/rest task by 6.88 to 82.19% (p < 0.001). The simulated online accuracy was also improved for most subjects (average accuracy for all subjects: 77.23% > 75.31%, and average F1_score for all subjects: 76.4% > 74.3%). The LMI-BCI paradigm of this study provides a novel approach to enhance LMI ability and accelerates the practical applications of the LMI-BCI system.
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Affiliation(s)
- Weizhen Wang
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Bin Shi
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Dong Wang
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Jing Wang
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Gang Liu
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
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Iwanami J, Mutai H, Sagari A, Sato M, Kobayashi M. Relationship between Corticospinal Excitability While Gazing at the Mirror and Motor Imagery Ability. Brain Sci 2023; 13:brainsci13030463. [PMID: 36979273 PMCID: PMC10046091 DOI: 10.3390/brainsci13030463] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/06/2023] [Accepted: 03/08/2023] [Indexed: 03/30/2023] Open
Abstract
Mirror therapy (MT) helps stroke survivors recover motor function. Previous studies have reported that an individual's motor imagery ability is related to the areas of brain activity during motor imagery and the effectiveness of motor imagery training. However, the relationship between MT and motor imagery ability and between corticospinal tract excitability during mirror gazing, an important component of MT, and motor imagery ability is unclear. This study determined whether the motor-evoked potential (MEP) amplitude while gazing at the mirror relates to participants' motor imagery abilities. Twenty-four healthy right-handed adults (seven males) were recruited. Transcranial magnetic stimulation was performed while gazing at the mirror, and MEP of the first dorsal interosseous muscle of the right hand were measured. Motor imagery ability was measured using the Kinesthetic and Visual Imagery Questionnaire (KVIQ), which assesses the vividness of motor imagery ability. Additionally, a mental chronometry (MC) task was used to assess time aspects. The results showed a significant moderate correlation between changes in MEP amplitude values while gazing at the mirror, as compared with resting conditions, and assessment scores of KVIQ. This study shows that corticospinal excitability because of mirror gazing may be related to the vividness of motor imagery ability.
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Affiliation(s)
- Jun Iwanami
- Division of Occupational Therapy, School of Health Science, Faculty of Medicine, Shinshu University, Matsumoto 390-8621, Japan
| | - Hitoshi Mutai
- Division of Occupational Therapy, School of Health Science, Faculty of Medicine, Shinshu University, Matsumoto 390-8621, Japan
| | - Akira Sagari
- Division of Occupational Therapy, School of Health Science, Faculty of Medicine, Shinshu University, Matsumoto 390-8621, Japan
| | - Masaaki Sato
- Division of Occupational Therapy, School of Health Science, Faculty of Medicine, Shinshu University, Matsumoto 390-8621, Japan
| | - Masayoshi Kobayashi
- Division of Occupational Therapy, School of Health Science, Faculty of Medicine, Shinshu University, Matsumoto 390-8621, Japan
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Tibrewal N, Leeuwis N, Alimardani M. Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users. PLoS One 2022; 17:e0268880. [PMID: 35867703 PMCID: PMC9307149 DOI: 10.1371/journal.pone.0268880] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 05/11/2022] [Indexed: 11/19/2022] Open
Abstract
Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain activity patterns associated with mental imagination of movement and convert them into commands for external devices. Traditionally, MI-BCIs operate on Machine Learning (ML) algorithms, which require extensive signal processing and feature engineering to extract changes in sensorimotor rhythms (SMR). In recent years, Deep Learning (DL) models have gained popularity for EEG classification as they provide a solution for automatic extraction of spatio-temporal features in the signals. However, past BCI studies that employed DL models, only attempted them with a small group of participants, without investigating the effectiveness of this approach for different user groups such as inefficient users. BCI inefficiency is a known and unsolved problem within BCI literature, generally defined as the inability of the user to produce the desired SMR patterns for the BCI classifier. In this study, we evaluated the effectiveness of DL models in capturing MI features particularly in the inefficient users. EEG signals from 54 subjects who performed a MI task of left- or right-hand grasp were recorded to compare the performance of two classification approaches; a ML approach vs. a DL approach. In the ML approach, Common Spatial Patterns (CSP) was used for feature extraction and then Linear Discriminant Analysis (LDA) model was employed for binary classification of the MI task. In the DL approach, a Convolutional Neural Network (CNN) model was constructed on the raw EEG signals. Additionally, subjects were divided into high vs. low performers based on their online BCI accuracy and the difference between the two classifiers’ performance was compared between groups. Our results showed that the CNN model improved the classification accuracy for all subjects within the range of 2.37 to 28.28%, but more importantly, this improvement was significantly larger for low performers. Our findings show promise for employment of DL models on raw EEG signals in future MI-BCI systems, particularly for BCI inefficient users who are unable to produce desired sensorimotor patterns for conventional ML approaches.
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Affiliation(s)
- Navneet Tibrewal
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, The Netherlands
| | - Nikki Leeuwis
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, The Netherlands
- Research Department, Unravel Research, Utrecht, The Netherlands
| | - Maryam Alimardani
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, The Netherlands
- * E-mail:
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13
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Tinaz S, Kamel S, Aravala SS, Elfil M, Bayoumi A, Patel A, Scheinost D, Sinha R, Hampson M. Neurofeedback-guided kinesthetic motor imagery training in Parkinson's disease: Randomized trial. Neuroimage Clin 2022; 34:102980. [PMID: 35247729 PMCID: PMC8897714 DOI: 10.1016/j.nicl.2022.102980] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 01/23/2022] [Accepted: 02/28/2022] [Indexed: 11/06/2022]
Abstract
Parkinson’s disease causes difficulty with sustained motor performance. Insula and dorsomedial frontal cortex (dmFC) are implicated in motivating movement. Regulation of insula-dmFC functional connectivity with neurofeedback (NF) failed. Motor imagery practice regardless of NF improved motor function and body awareness. Visual imagery practice without NF also improved motor function.
Background Parkinson’s disease (PD) causes difficulty with maintaining the speed, size, and vigor of movements, especially when they are internally generated. We previously proposed that the insula is important in motivating intentional movement via its connections with the dorsomedial frontal cortex (dmFC). We demonstrated that subjects with PD can increase the right insula-dmFC functional connectivity using fMRI-based neurofeedback (NF) combined with kinesthetic motor imagery (MI). The current study is a randomized clinical trial testing whether NF-guided kinesthetic MI training can improve motor performance and increase task-based and resting-state right insula-dmFC functional connectivity in subjects with PD. Methods We assigned nondemented subjects with mild PD (Hoehn & Yahr stage ≤ 3) to the experimental kinesthetic MI with NF (MI-NF, n = 22) and active control visual imagery (VI, n = 22) groups. Only the MI-NF group received NF-guided MI training (10–12 runs). The NF signal was based on the right insula-dmFC functional connectivity strength. All subjects also practiced their respective imagery tasks at home daily for 4 weeks. Post-training changes in 1) task-based and resting-state right insula-dmFC functional connectivity were the primary imaging outcomes, and 2) MDS-UPDRS motor exam and motor function scores were the primary and secondary clinical outcomes, respectively. Results The MI-NF group was not significantly different from the VI group in any of the primary imaging or clinical outcome measures. The MI-NF group reported subjective improvement in kinesthetic body awareness. There was significant and comparable improvement only in motor function scores in both groups (secondary clinical outcome). This improvement correlated with NF regulation of the right insula-dmFC functional connectivity only in the MI-NF group. Both groups showed specific training effects in whole-brain functional connectivity with distinct neural circuits supporting kinesthetic motor and visual imagery (exploratory imaging outcome). Conclusions The functional connectivity-based NF regulation was unsuccessful, however, both kinesthetic MI and VI practice improved motor function in our cohort with mild PD.
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Affiliation(s)
- Sule Tinaz
- Yale School of Medicine, Department of Neurology, Division of Movement Disorders, 15 York St, LCI 710, New Haven, CT 06510, USA; Yale School of Medicine, Clinical Neuroscience Imaging Center, 789 Howard Ave, New Haven, CT 06519, USA.
| | - Serageldin Kamel
- Yale School of Medicine, Department of Neurology, Division of Movement Disorders, 15 York St, LCI 710, New Haven, CT 06510, USA; Yale School of Medicine, Clinical Neuroscience Imaging Center, 789 Howard Ave, New Haven, CT 06519, USA
| | - Sai S Aravala
- Yale School of Medicine, Department of Neurology, Division of Movement Disorders, 15 York St, LCI 710, New Haven, CT 06510, USA; Yale School of Medicine, Clinical Neuroscience Imaging Center, 789 Howard Ave, New Haven, CT 06519, USA
| | - Mohamed Elfil
- Yale School of Medicine, Department of Neurology, Division of Movement Disorders, 15 York St, LCI 710, New Haven, CT 06510, USA; Yale School of Medicine, Clinical Neuroscience Imaging Center, 789 Howard Ave, New Haven, CT 06519, USA
| | - Ahmed Bayoumi
- Yale School of Medicine, Department of Neurology, Division of Movement Disorders, 15 York St, LCI 710, New Haven, CT 06510, USA; Yale School of Medicine, Clinical Neuroscience Imaging Center, 789 Howard Ave, New Haven, CT 06519, USA
| | - Amar Patel
- Yale School of Medicine, Department of Neurology, Division of Movement Disorders, 15 York St, LCI 710, New Haven, CT 06510, USA
| | - Dustin Scheinost
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, 300 Cedar St, New Haven, CT 06519, USA; Yale University, Department of Biomedical Engineering, 17 Hillhouse Avenue, New Haven, CT 06520, USA; Yale School of Medicine, Child Study Center, 230 South Frontage Road, New Haven, CT 06519, USA
| | - Rajita Sinha
- Yale School of Medicine, Yale Stress Center, 2 Church St South, Suite 209, New Haven, CT 06519, USA; Yale School of Medicine, Department of Psychiatry, 300 George St, New Haven, CT 06511, USA; Yale School of Medicine, Department of Neuroscience, 333 Cedar St, SHM-L-200, New Haven, CT 06510, USA
| | - Michelle Hampson
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, 300 Cedar St, New Haven, CT 06519, USA; Yale University, Department of Biomedical Engineering, 17 Hillhouse Avenue, New Haven, CT 06520, USA; Yale School of Medicine, Child Study Center, 230 South Frontage Road, New Haven, CT 06519, USA; Yale School of Medicine, Department of Psychiatry, 300 George St, New Haven, CT 06511, USA
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14
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Seebacher B, Helmlinger B, Pinter D, Ehling R, Hegen H, Ropele S, Reishofer G, Enzinger C, Brenneis C, Deisenhammer F. Effects of actual and imagined music-cued gait training on motor functioning and brain activity in people with multiple sclerosis: protocol of a randomised parallel multicentre trial. BMJ Open 2022; 12:e056666. [PMID: 35131834 PMCID: PMC8823210 DOI: 10.1136/bmjopen-2021-056666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
INTRODUCTION Motor imagery (MI) refers to the mental rehearsal of a physical action without muscular activity. Our previous studies showed that MI combined with rhythmic-auditory cues improved walking, fatigue and quality of life (QoL) in people with multiple sclerosis (pwMS). Largest improvements were seen after music and verbally cued MI. It is unclear whether actual cued gait training achieves similar effects on walking as cued MI in pwMS. Furthermore, in pwMS it is unknown whether any of these interventions leads to changes in brain activation. The purpose of this study is therefore to compare the effects of imagined and actual cued gait training and a combination thereof on walking, brain activation patterns, fatigue, cognitive and emotional functioning in pwMS. METHODS AND ANALYSIS A prospective double-blind randomised parallel multicentre trial will be conducted in 132 pwMS with mild to moderate disability. Randomised into three groups, participants will receive music, metronome and verbal cueing, plus MI of walking (1), MI combined with actual gait training (2) or actual gait training (3) for 30 min, 4× per week for 4 weeks. Supported by weekly phone calls, participants will practise at home, guided by recorded instructions. Primary endpoints will be walking speed (Timed 25-Foot Walk) and distance (2 min Walk Test). Secondary endpoints will be brain activation patterns, fatigue, QoL, MI ability, anxiety, depression, cognitive functioning, music-induced motivation-to-move, pleasure, arousal and self-efficacy. Data will be collected at baseline, postintervention and 3-month follow-up. MRI reference values will be generated using 15 matched healthy controls. ETHICS AND DISSEMINATION This study follows the Standard Protocol Items: Recommendations for Interventional Trials-PRO Extension. Ethical approval was received from the Ethics Committees of the Medical Universities of Innsbruck (1347/2020) and Graz (33-056 ex 20/21), Austria. Results will be disseminated via national and international conferences and published in peer-reviewed journals. TRIAL REGISTRATION NUMBER DRKS00023978.
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Affiliation(s)
- Barbara Seebacher
- Clinical Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
- Karl Landsteiner Institute for Interdisciplinary Rehabilitation Research, Münster, Austria
| | - Birgit Helmlinger
- Department of Neurology, Medical University of Graz, Graz, Austria
- Department of Neurology, Research Unit for Neuronal Plasticity and Repair, Medical University of Graz, Graz, Austria
| | - Daniela Pinter
- Department of Neurology, Medical University of Graz, Graz, Austria
- Department of Neurology, Research Unit for Neuronal Plasticity and Repair, Medical University of Graz, Graz, Austria
| | - Rainer Ehling
- Karl Landsteiner Institute for Interdisciplinary Rehabilitation Research, Münster, Austria
- Department of Neurology, Clinic for Rehabilitation Münster, Münster, Austria
| | - Harald Hegen
- Clinical Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Stefan Ropele
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Gernot Reishofer
- Department of Radiology, Division of Neuroradiology, Vascular and Interventional Radiology, Medical University of Graz, Graz, Austria
| | - Christian Enzinger
- Department of Neurology; Division of Neuroradiology; Department of Radiology, Medical University of Graz, Graz, Austria
| | - Christian Brenneis
- Karl Landsteiner Institute for Interdisciplinary Rehabilitation Research, Münster, Austria
- Department of Neurology, Clinic for Rehabilitation Münster, Münster, Austria
| | - Florian Deisenhammer
- Clinical Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
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15
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Tayeb Z, Dragomir A, Lee JH, Abbasi NI, Dean E, Bandla A, Bose R, Sundar R, Bezerianos A, Thakor NV, Cheng G. Distinct spatio-temporal and spectral brain patterns for different thermal stimuli perception. Sci Rep 2022; 12:919. [PMID: 35042875 PMCID: PMC8766611 DOI: 10.1038/s41598-022-04831-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 12/28/2021] [Indexed: 11/17/2022] Open
Abstract
Understanding the human brain's perception of different thermal sensations has sparked the interest of many neuroscientists. The identification of distinct brain patterns when processing thermal stimuli has several clinical applications, such as phantom-limb pain prediction, as well as increasing the sense of embodiment when interacting with neurorehabilitation devices. Notwithstanding the remarkable number of studies that have touched upon this research topic, understanding how the human brain processes different thermal stimuli has remained elusive. More importantly, very intense thermal stimuli perception dynamics, their related cortical activations, as well as their decoding using effective features are still not fully understood. In this study, using electroencephalography (EEG) recorded from three healthy human subjects, we identified spatial, temporal, and spectral patterns of brain responses to different thermal stimulations ranging from extremely cold and hot stimuli (very intense), moderately cold and hot stimuli (intense), to a warm stimulus (innocuous). Our results show that very intense thermal stimuli elicit a decrease in alpha power compared to intense and innocuous stimulations. Spatio-temporal analysis reveals that in the first 400 ms post-stimulus, brain activity increases in the prefrontal and central brain areas for very intense stimulations, whereas for intense stimulation, high activity of the parietal area was observed post-500 ms. Based on these identified EEG patterns, we successfully classified the different thermal stimulations with an average test accuracy of 84% across all subjects. En route to understanding the underlying cortical activity, we source localized the EEG signal for each of the five thermal stimuli conditions. Our findings reveal that very intense stimuli were anticipated and induced early activation (before 400 ms) of the anterior cingulate cortex (ACC). Moreover, activation of the pre-frontal cortex, somatosensory, central, and parietal areas, was observed in the first 400 ms post-stimulation for very intense conditions and starting 500 ms post-stimuli for intense conditions. Overall, despite the small sample size, this work presents novel findings and a first comprehensive approach to explore, analyze, and classify EEG-brain activity changes evoked by five different thermal stimuli, which could lead to a better understanding of thermal stimuli processing in the brain and could, therefore, pave the way for developing a real-time withdrawal reaction system when interacting with prosthetic limbs. We underpin this last point by benchmarking our EEG results with a demonstration of a real-time withdrawal reaction of a robotic prosthesis using a human-like artificial skin.
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Affiliation(s)
- Zied Tayeb
- Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany.
| | - Andrei Dragomir
- The N.1 Institute for Health, National University of Singapore, 28 Medical Dr. 05-COR, Singapore, 117456, Singapore
- Department of Biomedical Engineering, University of Houston, 3517 Cullen Blvd, Houston, TX, 77204, USA
| | - Jin Ho Lee
- Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany
| | - Nida Itrat Abbasi
- The N.1 Institute for Health, National University of Singapore, 28 Medical Dr. 05-COR, Singapore, 117456, Singapore
| | - Emmanuel Dean
- Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany
- Chalmers University of Technology, 412 96, Gothenburg, Sweden
| | - Aishwarya Bandla
- The N.1 Institute for Health, National University of Singapore, 28 Medical Dr. 05-COR, Singapore, 117456, Singapore
| | - Rohit Bose
- Department of Bioengineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, PA, 15261, USA
| | - Raghav Sundar
- The N.1 Institute for Health, National University of Singapore, 28 Medical Dr. 05-COR, Singapore, 117456, Singapore
- Department of Haematology-Oncology, National University Cancer Institute, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Anastasios Bezerianos
- The N.1 Institute for Health, National University of Singapore, 28 Medical Dr. 05-COR, Singapore, 117456, Singapore
- Hellenic Institute of Transport (HIT), Centre for Research and Technology (CERTH), Thessaloniki, Greece
| | - Nitish V Thakor
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, 720 Rutland Ave, Baltimore, MD, 21205, USA
- Department of Biomedical Engineering, National University of Singapore, Engineering Drive 3, #04-08, Singapore, 117583, Singapore
| | - Gordon Cheng
- Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany
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16
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Rannaud Monany D, Barbiero M, Lebon F, Babič J, Blohm G, Nozaki D, White O. Motor imagery helps updating internal models during microgravity exposure. J Neurophysiol 2022; 127:434-443. [PMID: 34986019 DOI: 10.1152/jn.00214.2021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Skilled movements result from a mixture of feedforward and feedback mechanisms conceptualized by internal models. These mechanisms subserve both motor execution and motor imagery. Current research suggests that imagery allows updating feedforward mechanisms, leading to better performance in familiar contexts. Does this still hold in radically new contexts? Here, we test this ability by asking participants to imagine swinging arm movements around shoulder in normal gravity condition and in microgravity in which studies showed that movements slow down. We timed several cycles of actual and imagined arm pendular movements in three groups of subjects during parabolic flight campaign. The first, control, group remained on the ground. The second group was exposed to microgravity but did not imagine movements inflight. The third group was exposed to microgravity and imagined movements inflight. All groups performed and imagined the movements before and after the flight. We predicted that a mere exposure to microgravity would induce changes in imagined movement duration. We found this held true for the group who imagined the movements, suggesting an update of internal representations of gravity. However, we did not find a similar effect in the group exposed to microgravity despite the fact participants lived the same gravitational variations as the first group. Overall, these results suggest that motor imagery contributes to update internal representations of movement in unfamiliar environments, while a mere exposure proved to be insufficient.
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Affiliation(s)
- Dylan Rannaud Monany
- Cognition, Action, and Sensorimotor Plasticity, University of Burgundy, Dijon, France
| | - Marie Barbiero
- Cognition, Action, and Sensorimotor Plasticity, University of Burgundy, Dijon, France.,Centre National d'Etudes Spatiales, University of Burgundy, Dijon, France
| | - Florent Lebon
- Cognition, Action, and Sensorimotor Plasticity, University of Burgundy, Dijon, France
| | - Jan Babič
- Laboratory for Neuromechanics and Biorobotics, Department of Automation, Biocybernetics and Robotics, Jožef Stefan Institute, Ljubljana, Slovenia
| | - Gunnar Blohm
- Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada
| | - Daichi Nozaki
- Graduate School of Education, The University of Tokyo, Tokyo, Japan
| | - Olivier White
- Cognition, Action, and Sensorimotor Plasticity, University of Burgundy, Dijon, France
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17
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Cuomo G, Maglianella V, Ghanbari Ghooshchy S, Zoccolotti P, Martelli M, Paolucci S, Morone G, Iosa M. Motor imagery and gait control in Parkinson's disease: techniques and new perspectives in neurorehabilitation. Expert Rev Neurother 2021; 22:43-51. [PMID: 34906019 DOI: 10.1080/14737175.2022.2018301] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Motor imagery (MI), defined as the ability to mentally represent an action without actual movement, has been used to improve motor function in athletes and, more recently, in neurological disorders such as Parkinson's disease (PD). Several studies have investigated the neural correlates of motor imagery, which change also depending on the action imagined. AREAS COVERED This review focuses on locomotion, which is a crucial activity in everyday life and is often impaired by neurological conditions. After a general discussion on the neural correlates of motor imagery and locomotion, we review the evidence highlighting the abnormalities in gait control and gait imagery in PD patients. Next, new perspectives and techniques for PD patients' rehabilitation are discussed, namely Brain Computer Interfaces (BCIs), neurofeedback, and virtual reality (VR). EXPERT OPINION Despite the few studies, the literature review supports the potential beneficial effects of motor imagery interventions in PD focused on locomotion. The development of new technologies could empower the administration of training based on motor imagery locomotor tasks, and their application could lead to new rehabilitation protocols aimed at improving walking ability in patients with PD.
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Affiliation(s)
- Giovanna Cuomo
- Department of Psychology, University of Rome "Sapienza", Rome, Italy
| | | | - Sheida Ghanbari Ghooshchy
- Department of Psychology, University of Rome "Sapienza", Rome, Italy.,IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Pierluigi Zoccolotti
- Department of Psychology, University of Rome "Sapienza", Rome, Italy.,IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Marialuisa Martelli
- Department of Psychology, University of Rome "Sapienza", Rome, Italy.,IRCCS Fondazione Santa Lucia, Rome, Italy
| | | | | | - Marco Iosa
- Department of Psychology, University of Rome "Sapienza", Rome, Italy.,IRCCS Fondazione Santa Lucia, Rome, Italy
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18
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Sugino H, Ushiyama J. Gymnasts' Ability to Modulate Sensorimotor Rhythms During Kinesthetic Motor Imagery of Sports Non-specific Movements Superior to Non-gymnasts. Front Sports Act Living 2021; 3:757308. [PMID: 34805979 PMCID: PMC8600039 DOI: 10.3389/fspor.2021.757308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 09/30/2021] [Indexed: 11/30/2022] Open
Abstract
Previous psychological studies using questionnaires have consistently reported that athletes have superior motor imagery ability, both for sports-specific and for sports-non-specific movements. However, regarding motor imagery of sports-non-specific movements, no physiological studies have demonstrated differences in neural activity between athletes and non-athletes. The purpose of this study was to examine the differences in sensorimotor rhythms during kinesthetic motor imagery (KMI) of sports-non-specific movements between gymnasts and non-gymnasts. We selected gymnasts as an example population because they are likely to have particularly superior motor imagery ability due to frequent usage of motor imagery, including KMI as part of daily practice. Healthy young participants (16 gymnasts and 16 non-gymnasts) performed repeated motor execution and KMI of sports-non-specific movements (wrist dorsiflexion and shoulder abduction of the dominant hand). Scalp electroencephalogram (EEG) was recorded over the contralateral sensorimotor cortex. During motor execution and KMI, sensorimotor EEG power is known to decrease in the α- (8–15 Hz) and β-bands (16–35 Hz), referred to as event-related desynchronization (ERD). We calculated the maximal peak of ERD both in the α- (αERDmax) and β-bands (βERDmax) as a measure of changes in corticospinal excitability. αERDmax was significantly greater in gymnasts, who subjectively evaluated their KMI as being more vivid in the psychological questionnaire. On the other hand, βERDmax was greater in gymnasts only for shoulder abduction KMI. These findings suggest gymnasts' signature of flexibly modulating sensorimotor rhythms with no movements, which may be the basis of their superior ability of KMI for sports-non-specific movements.
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Affiliation(s)
- Hirotaka Sugino
- Graduate School of Media and Governance, Keio University, Fujisawa, Japan
| | - Junichi Ushiyama
- Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan.,Department of Rehabilitation Medicine, Keio University School of Medicine, Tokyo, Japan
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19
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Zhang K, Xu G, Du C, Liang R, Han C, Zheng X, Zhang S, Wang J, Tian P, Jia Y. Enhancement of capability for motor imagery using vestibular imbalance stimulation during brain computer interface. J Neural Eng 2021; 18. [PMID: 34571497 DOI: 10.1088/1741-2552/ac2a6f] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 09/27/2021] [Indexed: 01/07/2023]
Abstract
Objective.Motor imagery (MI), based on the theory of mirror neurons and neuroplasticity, can promote motor cortical activation in neurorehabilitation. The strategy of MI based on brain-computer interface (BCI) has been used in rehabilitation training and daily assistance for patients with hemiplegia in recent years. However, it is difficult to maintain the consistency and timeliness of receiving external stimulation to neural activation in most subjects owing to the high variability of electroencephalogram (EEG) representation across trials/subjects. Moreover, in practical application, MI-BCI cannot highly activate the motor cortex and provide stable interaction owing to the weakness of the EEG feature and lack of an effective mode of activation.Approach.In this study, a novel hybrid BCI paradigm based on MI and vestibular stimulation motor imagery (VSMI) was proposed to enhance the capability of feature response for MI. Twelve subjects participated in a group of controlled experiments containing VSMI and MI. Three indicators, namely, activation degree, timeliness, and classification accuracy, were adopted to evaluate the performance of the task.Main results.Vestibular stimulation could significantly strengthen the suppression ofαandβbands of contralateral brain regions during MI, that is, enhance the activation degree of the motor cortex (p< 0.01). Compared with MI, the timeliness of EEG feature-response achieved obvious improvements in VSMI experiments. Moreover, the averaged classification accuracy of VSMI and MI was 80.56% and 69.38%, respectively.Significance.The experimental results indicate that specific vestibular activity contributes to the oscillations of the motor cortex and has a positive effect on spontaneous imagery, which provides a novel MI paradigm and enables the preliminary exploration of sensorimotor integration of MI.
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Affiliation(s)
- Kai Zhang
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Guanghua Xu
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China.,State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Chenghang Du
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Renghao Liang
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Chenchen Han
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Xiaowei Zheng
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Sicong Zhang
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Jiahuan Wang
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Peiyuan Tian
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Yaguang Jia
- School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China
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20
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Leeuwis N, Yoon S, Alimardani M. Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces. Front Hum Neurosci 2021; 15:732946. [PMID: 34720907 PMCID: PMC8555469 DOI: 10.3389/fnhum.2021.732946] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/03/2021] [Indexed: 11/25/2022] Open
Abstract
Motor Imagery BCI systems have a high rate of users that are not capable of modulating their brain activity accurately enough to communicate with the system. Several studies have identified psychological, cognitive, and neurophysiological measures that might explain this MI-BCI inefficiency. Traditional research had focused on mu suppression in the sensorimotor area in order to classify imagery, but this does not reflect the true dynamics that underlie motor imagery. Functional connectivity reflects the interaction between brain regions during the MI task and resting-state network and is a promising tool in improving MI-BCI classification. In this study, 54 novice MI-BCI users were split into two groups based on their accuracy and their functional connectivity was compared in three network scales (Global, Large and Local scale) during the resting-state, left vs. right-hand motor imagery task, and the transition between the two phases. Our comparison of High and Low BCI performers showed that in the alpha band, functional connectivity in the right hemisphere was increased in High compared to Low aptitude MI-BCI users during motor imagery. These findings contribute to the existing literature that indeed connectivity might be a valuable feature in MI-BCI classification and in solving the MI-BCI inefficiency problem.
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Affiliation(s)
- Nikki Leeuwis
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, Netherlands
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Le Franc S, Fleury M, Jeunet C, Butet S, Barillot C, Bonan I, Cogné M, Lécuyer A. Influence of the visuo-proprioceptive illusion of movement and motor imagery of the wrist on EEG cortical excitability among healthy participants. PLoS One 2021; 16:e0256723. [PMID: 34473788 PMCID: PMC8412266 DOI: 10.1371/journal.pone.0256723] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Accepted: 08/13/2021] [Indexed: 11/18/2022] Open
Abstract
INTRODUCTION Motor Imagery (MI) is a powerful tool to stimulate sensorimotor brain areas and is currently used in motor rehabilitation after a stroke. The aim of our study was to evaluate whether an illusion of movement induced by visuo-proprioceptive immersion (VPI) including tendon vibration (TV) and Virtual moving hand (VR) combined with MI tasks could be more efficient than VPI alone or MI alone on cortical excitability assessed using Electroencephalography (EEG). METHODS We recorded EEG signals in 20 healthy participants in 3 different conditions: MI tasks involving their non-dominant wrist (MI condition); VPI condition; and VPI with MI tasks (combined condition). Each condition lasted 3 minutes, and was repeated 3 times in randomized order. Our main judgment criterion was the Event-Related De-synchronization (ERD) threshold in sensori-motor areas in each condition in the brain motor area. RESULTS The combined condition induced a greater change in the ERD percentage than the MI condition alone, but no significant difference was found between the combined and the VPI condition (p = 0.07) and between the VPI and MI condition (p = 0.20). CONCLUSION This study demonstrated the interest of using a visuo-proprioceptive immersion with MI rather than MI alone in order to increase excitability in motor areas of the brain. Further studies could test this hypothesis among patients with stroke to provide new perspectives for motor rehabilitation in this population.
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Affiliation(s)
- Salomé Le Franc
- Rehabilitation Medicine Unit, University Hospital of Rennes, Rennes, France
- Hybrid Team, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
| | - Mathis Fleury
- Hybrid Team, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
- Empenn Unit U1228, Inserm, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
| | - Camille Jeunet
- CLLE Lab, CNRS, Univ. Toulouse Jean Jaurès, Toulouse, France
| | - Simon Butet
- Rehabilitation Medicine Unit, University Hospital of Rennes, Rennes, France
- Empenn Unit U1228, Inserm, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
| | - Christian Barillot
- Empenn Unit U1228, Inserm, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
| | - Isabelle Bonan
- Rehabilitation Medicine Unit, University Hospital of Rennes, Rennes, France
- Empenn Unit U1228, Inserm, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
| | - Mélanie Cogné
- Rehabilitation Medicine Unit, University Hospital of Rennes, Rennes, France
- Hybrid Team, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
| | - Anatole Lécuyer
- Hybrid Team, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
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Simon C, Bolton DAE, Kennedy NC, Soekadar SR, Ruddy KL. Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation. Front Neurosci 2021; 15:699428. [PMID: 34276299 PMCID: PMC8282929 DOI: 10.3389/fnins.2021.699428] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/08/2021] [Indexed: 12/18/2022] Open
Abstract
Brain-computer interfaces (BCIs) provide a unique technological solution to circumvent the damaged motor system. For neurorehabilitation, the BCI can be used to translate neural signals associated with movement intentions into tangible feedback for the patient, when they are unable to generate functional movement themselves. Clinical interest in BCI is growing rapidly, as it would facilitate rehabilitation to commence earlier following brain damage and provides options for patients who are unable to partake in traditional physical therapy. However, substantial challenges with existing BCI implementations have prevented its widespread adoption. Recent advances in knowledge and technology provide opportunities to facilitate a change, provided that researchers and clinicians using BCI agree on standardisation of guidelines for protocols and shared efforts to uncover mechanisms. We propose that addressing the speed and effectiveness of learning BCI control are priorities for the field, which may be improved by multimodal or multi-stage approaches harnessing more sensitive neuroimaging technologies in the early learning stages, before transitioning to more practical, mobile implementations. Clarification of the neural mechanisms that give rise to improvement in motor function is an essential next step towards justifying clinical use of BCI. In particular, quantifying the unknown contribution of non-motor mechanisms to motor recovery calls for more stringent control conditions in experimental work. Here we provide a contemporary viewpoint on the factors impeding the scalability of BCI. Further, we provide a future outlook for optimal design of the technology to best exploit its unique potential, and best practices for research and reporting of findings.
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Affiliation(s)
- Colin Simon
- Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - David A. E. Bolton
- Department of Kinesiology and Health Science, Utah State University, Logan, UT, United States
| | - Niamh C. Kennedy
- School of Psychology, Ulster University, Coleraine, United Kingdom
| | - Surjo R. Soekadar
- Clinical Neurotechnology Laboratory, Neurowissenschaftliches Forschungszentrum, Department of Psychiatry and Neurosciences, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Kathy L. Ruddy
- Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, Dublin, Ireland
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Idowu OP, Adelopo O, Ilesanmi AE, Li X, Samuel OW, Fang P, Li G. Neuro-evolutionary approach for optimal selection of EEG channels in motor imagery based BCI application. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102621] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Castro F, Bryjka PA, Di Pino G, Vuckovic A, Nowicky A, Bishop D. Sonification of combined action observation and motor imagery: Effects on corticospinal excitability. Brain Cogn 2021; 152:105768. [PMID: 34144438 DOI: 10.1016/j.bandc.2021.105768] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 05/26/2021] [Accepted: 05/28/2021] [Indexed: 01/06/2023]
Abstract
Action observation and motor imagery are valuable strategies for motor learning. Their simultaneous use (AOMI) increases neural activity, with related benefits for motor learning, compared to the two strategies alone. In this study, we explored how sonification influences AOMI. Twenty-five participants completed a practice block based on AOMI, motor imagery and physical execution of the same action. Participants were divided into two groups: An experimental group that practiced with sonification during AOMI (sAOMI), and a control group, which did not receive any extrinsic feedback. Corticospinal excitability at rest and during action observation and AOMI was assessed before and after practice, with and without sonification sound, to test the development of an audiomotor association. The practice block increased corticospinal excitability in all testing conditions, but sonification did not affect this. In addition, we found no differences in action observation and AOMI, irrespective of sonification. These results suggest that, at least for simple tasks, sonification of AOMI does not influence corticospinal excitability; In these conditions, sonification may have acted as a distractor. Future studies should further explore the relationship between task complexity, value of auditory information and action, to establish whether sAOMI is a valuable for motor learning.
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Affiliation(s)
- Fabio Castro
- Research Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction (Next Lab), Università Campus Bio-Medico di Roma, Rome, Italy; Centre for Cognitive Neuroscience, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge, UK.
| | - Paulina Anna Bryjka
- Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge, UK
| | - Giovanni Di Pino
- Research Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction (Next Lab), Università Campus Bio-Medico di Roma, Rome, Italy
| | - Aleksandra Vuckovic
- School of Engineering, College of Engineering and Science, James Watt Building (south) University of Glasgow, Glasgow G12 8QQ, UK
| | - Alexander Nowicky
- Centre for Cognitive Neuroscience, Department of Clinical Sciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge, UK
| | - Daniel Bishop
- Centre for Cognitive Neuroscience, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge, UK
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25
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Buch ER, Claudino L, Quentin R, Bönstrup M, Cohen LG. Consolidation of human skill linked to waking hippocampo-neocortical replay. Cell Rep 2021; 35:109193. [PMID: 34107255 PMCID: PMC8259719 DOI: 10.1016/j.celrep.2021.109193] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/10/2021] [Accepted: 05/09/2021] [Indexed: 01/05/2023] Open
Abstract
The introduction of rest intervals interspersed with practice strengthens wakeful consolidation of skill. The mechanisms by which the brain binds discrete action representations into consolidated, highly temporally resolved skill sequences during waking rest are not known. To address this question, we recorded magnetoencephalography (MEG) during acquisition and rapid consolidation of a sequential motor skill. We report the presence of prominent, fast waking neural replay during the same rest periods in which rapid consolidation occurs. The observed replay is temporally compressed by approximately 20-fold relative to the acquired skill, is selective for the trained sequence, and predicts the magnitude of skill consolidation. Replay representations extend beyond the hippocampus and entorhinal cortex to the contralateral sensorimotor cortex. These results document the presence of robust hippocampo-neocortical replay supporting rapid wakeful consolidation of skill.
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Affiliation(s)
- Ethan R Buch
- Human Cortical Physiology and Neurorehabilitation Section, NINDS, NIH, Bethesda, MD, USA.
| | - Leonardo Claudino
- Human Cortical Physiology and Neurorehabilitation Section, NINDS, NIH, Bethesda, MD, USA
| | - Romain Quentin
- Human Cortical Physiology and Neurorehabilitation Section, NINDS, NIH, Bethesda, MD, USA
| | - Marlene Bönstrup
- Human Cortical Physiology and Neurorehabilitation Section, NINDS, NIH, Bethesda, MD, USA
| | - Leonardo G Cohen
- Human Cortical Physiology and Neurorehabilitation Section, NINDS, NIH, Bethesda, MD, USA.
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Castro F, Osman L, Di Pino G, Vuckovic A, Nowicky A, Bishop D. Does sonification of action simulation training impact corticospinal excitability and audiomotor plasticity? Exp Brain Res 2021; 239:1489-1505. [PMID: 33683403 PMCID: PMC8144125 DOI: 10.1007/s00221-021-06069-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 02/19/2021] [Indexed: 01/03/2023]
Abstract
Sonification is a sensory augmentation strategy whereby a sound is associated with, and modulated by, movement. Evidence suggests that sonification could be a viable strategy to maximize learning and rehabilitation. Recent studies investigated sonification of action observation, reporting beneficial effects, especially in Parkinson's disease. However, research on simulation training-a training regime based on action observation and motor imagery, in which actions are internally simulated, without physical execution-suggest that action observation alone is suboptimal, compared to the combined use of action observation and motor imagery. In this study, we explored the effects of sonified action observation and motor imagery on corticospinal excitability, as well as to evaluate the extent of practice-dependent plasticity induced by this training. Nineteen participants were recruited to complete a practice session based on combined and congruent action observation and motor imagery (AOMI) and physical imitation of the same action. Prior to the beginning, participants were randomly assigned to one of two groups, one group (nine participants) completed the practice block with sonified AOMI, while the other group (ten participants) completed the practice without extrinsic auditory information and served as control group. To investigate practice-induced plasticity, participants completed two auditory paired associative stimulation (aPAS) protocols, one completed after the practice block, and another one completed alone, without additional interventions, at least 7 days before the practice. After the practice block, both groups significantly increased their corticospinal excitability, but sonification did not exert additional benefits, compared to non-sonified conditions. In addition, aPAS significantly increased corticospinal excitability when completed alone, but when it was primed by a practice block, no modulatory effects on corticospinal excitability were found. It is possible that sonification of combined action observation and motor imagery may not be a useful strategy to improve corticospinal, but further studies are needed to explore its relationship with performance improvements. We also confirm the neuromodulatory effect of aPAS, but its interaction with audiomotor practice remain unclear.
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Affiliation(s)
- Fabio Castro
- Research Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction (NeXTlab), Università Campus Bio-Medico Di Roma, Rome, Italy.
- Centre for Cognitive Neuroscience, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge, UK.
| | - Ladan Osman
- Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge, UK
| | - Giovanni Di Pino
- Research Unit of Neurophysiology and Neuroengineering of Human-Technology Interaction (NeXTlab), Università Campus Bio-Medico Di Roma, Rome, Italy
| | - Aleksandra Vuckovic
- School of Engineering, College of Engineering and Science, James Watt Building (South) University of Glasgow, Glasgow, G12 8QQ, UK
| | - Alexander Nowicky
- Centre for Cognitive Neuroscience, Department of Clinical Sciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge, UK
| | - Daniel Bishop
- Centre for Cognitive Neuroscience, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge, UK
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27
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Leeuwis N, Paas A, Alimardani M. Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces. Front Hum Neurosci 2021; 15:634748. [PMID: 33889080 PMCID: PMC8055841 DOI: 10.3389/fnhum.2021.634748] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 03/08/2021] [Indexed: 12/19/2022] Open
Abstract
Brain-computer interfaces (BCIs) are communication bridges between a human brain and external world, enabling humans to interact with their environment without muscle intervention. Their functionality, therefore, depends on both the BCI system and the cognitive capacities of the user. Motor-imagery BCIs (MI-BCI) rely on the users' mental imagination of body movements. However, not all users have the ability to sufficiently modulate their brain activity for control of a MI-BCI; a problem known as BCI illiteracy or inefficiency. The underlying mechanism of this phenomenon and the cause of such difference among users is yet not fully understood. In this study, we investigated the impact of several cognitive and psychological measures on MI-BCI performance. Fifty-five novice BCI-users participated in a left- versus right-hand motor imagery task. In addition to their BCI classification error rate and demographics, psychological measures including personality factors, affinity for technology, and motivation during the experiment, as well as cognitive measures including visuospatial memory and spatial ability and Vividness of Visual Imagery were collected. Factors that were found to have a significant impact on MI-BCI performance were Vividness of Visual Imagery, and the personality factors of orderliness and autonomy. These findings shed light on individual traits that lead to difficulty in BCI operation and hence can help with early prediction of inefficiency among users to optimize training for them.
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Affiliation(s)
- Nikki Leeuwis
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, Netherlands
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28
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Singanamalla SKR, Lin CT. Spiking Neural Network for Augmenting Electroencephalographic Data for Brain Computer Interfaces. Front Neurosci 2021; 15:651762. [PMID: 33867928 PMCID: PMC8047134 DOI: 10.3389/fnins.2021.651762] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 02/22/2021] [Indexed: 11/28/2022] Open
Abstract
With the advent of advanced machine learning methods, the performance of brain–computer interfaces (BCIs) has improved unprecedentedly. However, electroencephalography (EEG), a commonly used brain imaging method for BCI, is characterized by a tedious experimental setup, frequent data loss due to artifacts, and is time consuming for bulk trial recordings to take advantage of the capabilities of deep learning classifiers. Some studies have tried to address this issue by generating artificial EEG signals. However, a few of these methods are limited in retaining the prominent features or biomarker of the signal. And, other deep learning-based generative methods require a huge number of samples for training, and a majority of these models can handle data augmentation of one category or class of data at any training session. Therefore, there exists a necessity for a generative model that can generate synthetic EEG samples with as few available trials as possible and generate multi-class while retaining the biomarker of the signal. Since EEG signal represents an accumulation of action potentials from neuronal populations beneath the scalp surface and as spiking neural network (SNN), a biologically closer artificial neural network, communicates via spiking behavior, we propose an SNN-based approach using surrogate-gradient descent learning to reconstruct and generate multi-class artificial EEG signals from just a few original samples. The network was employed for augmenting motor imagery (MI) and steady-state visually evoked potential (SSVEP) data. These artificial data are further validated through classification and correlation metrics to assess its resemblance with original data and in-turn enhanced the MI classification performance.
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Affiliation(s)
- Sai Kalyan Ranga Singanamalla
- Computational Intelligence and Brain Computer Interface Lab, School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia
| | - Chin-Teng Lin
- Computational Intelligence and Brain Computer Interface Lab, School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia.,Centre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW, Australia
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29
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Multi-Session Influence of Two Modalities of Feedback and Their Order of Presentation on MI-BCI User Training. MULTIMODAL TECHNOLOGIES AND INTERACTION 2021. [DOI: 10.3390/mti5030012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
By performing motor-imagery tasks, for example, imagining hand movements, Motor-Imagery based Brain-Computer Interfaces (MI-BCIs) users can control digital technologies, for example, neuroprosthesis, using their brain activity only. MI-BCI users need to train, usually using a unimodal visual feedback, to produce brain activity patterns that are recognizable by the system. The literature indicates that multimodal vibrotactile and visual feedback is more effective than unimodal visual feedback, at least for short term training. However, the multi-session influence of such multimodal feedback on MI-BCI user training remained unknown, so did the influence of the order of presentation of the feedback modalities. In our experiment, 16 participants trained to control a MI-BCI during five sessions with a realistic visual feedback and five others with both a realistic visual feedback and a vibrotactile one. training benefits from a multimodal feedback, in terms of performances and self-reported mindfulness. There is also a significant influence of the order presentation of the modality. Participants who started training with a visual feedback had higher performances than those who started training with a multimodal feedback. We recommend taking into account the order of presentation for future experiments assessing the influence of several modalities of feedback.
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Saha S, Mamun KA, Ahmed K, Mostafa R, Naik GR, Darvishi S, Khandoker AH, Baumert M. Progress in Brain Computer Interface: Challenges and Opportunities. Front Syst Neurosci 2021; 15:578875. [PMID: 33716680 PMCID: PMC7947348 DOI: 10.3389/fnsys.2021.578875] [Citation(s) in RCA: 122] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 01/06/2021] [Indexed: 12/13/2022] Open
Abstract
Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.
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Affiliation(s)
- Simanto Saha
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
- Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh
| | - Khondaker A. Mamun
- Advanced Intelligent Multidisciplinary Systems (AIMS) Lab, Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh
| | - Khawza Ahmed
- Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh
| | - Raqibul Mostafa
- Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh
| | - Ganesh R. Naik
- Adelaide Institute for Sleep Health, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Sam Darvishi
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
| | - Ahsan H. Khandoker
- Healthcare Engineering Innovation Center, Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Mathias Baumert
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
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Formica S, González-García C, Senoussi M, Brass M. Neural oscillations track the maintenance and proceduralization of novel instructions. Neuroimage 2021; 232:117870. [PMID: 33607280 DOI: 10.1016/j.neuroimage.2021.117870] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 01/26/2021] [Accepted: 02/11/2021] [Indexed: 12/30/2022] Open
Abstract
Humans are capable of flexibly converting symbolic instructions into novel behaviors. Previous evidence and theoretical models suggest that the implementation of a novel instruction requires the reformatting of its declarative content into an action-oriented code optimized for the execution of the instructed behavior. While neuroimaging research focused on identifying the brain areas involved in such a process, the temporal and electrophysiological mechanisms remain poorly understood. These mechanisms, however, can provide information about the specific cognitive processes that characterize the proceduralization of information. In the present study, we recorded EEG activity while we asked participants to either simply maintain declaratively the content of novel S-R mappings or to proactively prepare for their implementation. By means of time-frequency analyses, we isolated the oscillatory features specific to the proceduralization of instructions. Implementation of the instructed mappings elicited stronger theta activity over frontal electrodes and suppression in mu and beta activity over central electrodes. On the contrary, activity in the alpha band, which has been shown to track the attentional deployment to task-relevant items, showed no differences between tasks. Together, these results support the idea that proceduralization of information is characterized by specific component processes such as orchestrating complex task settings and configuring the motor system that are not observed when instructions are held in a declarative format.
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Affiliation(s)
- Silvia Formica
- Department of Experimental Psychology, Ghent University, Belgium.
| | | | - Mehdi Senoussi
- Department of Experimental Psychology, Ghent University, Belgium
| | - Marcel Brass
- Department of Experimental Psychology, Ghent University, Belgium; School of Mind and Brain/Department of Psychology, Humboldt Universität zu Berlin, Germany
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Zhang R, Li F, Zhang T, Yao D, Xu P. Subject inefficiency phenomenon of motor imagery brain-computer interface: Influence factors and potential solutions. BRAIN SCIENCE ADVANCES 2021. [DOI: 10.26599/bsa.2020.9050021] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Motor imagery brain–computer interfaces (MI‐BCIs) have great potential value in prosthetics control, neurorehabilitation, and gaming; however, currently, most such systems only operate in controlled laboratory environments. One of the most important obstacles is the MI‐BCI inefficiency phenomenon. The accuracy of MI‐BCI control varies significantly (from chance level to 100% accuracy) across subjects due to the not easily induced and unstable MI‐related EEG features. An MI‐BCI inefficient subject is defined as a subject who cannot achieve greater than 70% accuracy after sufficient training time, and multiple survey results indicate that inefficient subjects account for 10%–50% of the experimental population. The widespread use of MI‐BCI has been seriously limited due to these large percentages of inefficient subjects. In this review, we summarize recent findings of the cause of MI‐BCI inefficiency from resting‐state brain function, task‐related brain activity, brain structure, and psychological perspectives. These factors help understand the reasons for inter‐subject MI‐BCI control performance variability, and it can be concluded that the lower resting‐state sensorimotor rhythm (SMR) is the key factor in MI‐BCI inefficiency, which has been confirmed by multiple independent laboratories. We then propose to divide MI‐BCI inefficient subjects into three categories according to the resting‐state SMR and offline/online accuracy to apply more accurate approaches to solve the inefficiency problem. The potential solutions include developing transfer learning algorithms, new experimental paradigms, mindfulness meditation practice, novel training strategies, and identifying new motor imagery‐related EEG features. To date, few studies have focused on improving the control accuracy of MI‐BCI inefficient subjects; thus, we appeal to the BCI community to focus more on this research area. Only by reducing the percentage of inefficient subjects can we create the opportunity to expand the value and influence of MI‐BCI.
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Affiliation(s)
- Rui Zhang
- Henan Key Laboratory of Brain Science and Brain‐Computer Interface Technology, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Fali Li
- MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China
| | - Tao Zhang
- Science of School, Xihua University, Chengdu 610039, Sichuan, China
| | - Dezhong Yao
- Henan Key Laboratory of Brain Science and Brain‐Computer Interface Technology, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, Henan, China
- MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China
| | - Peng Xu
- MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China
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Trapero-Asenjo S, Gallego-Izquierdo T, Pecos-Martín D, Nunez-Nagy S. Translation, cultural adaptation, and validation of the Spanish version of the Movement Imagery Questionnaire-3 (MIQ-3). Musculoskelet Sci Pract 2021; 51:102313. [PMID: 33310512 DOI: 10.1016/j.msksp.2020.102313] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 11/24/2020] [Accepted: 11/27/2020] [Indexed: 01/10/2023]
Abstract
OBJECTIVE The goal of this study was to translate, culturally adapt, and validate the Spanish version of the Movement Imagery Questionnaire-3 in order to assess an individual's external visual, internal, and kinesthetic imagery abilities. DESIGN Prospective two-phase scale validation study. SUBJECTS One-hundred and forty subjects (47 men and 93 women, mean age = 21.54 ± 2.127 years) were included in the study. METHODS A direct and indirect translation of the questionnaire was initially carried out and then the psychometric properties of the questionnaire were studied. RESULTS The confirmatory factor analysis showed a good model fit. The percentage of explained variance was 69.55. Good internal consistency was observed for the total score and for each subscale (internal visual = 0.849, external visual = 0.837 and kinesthetic = 0.857). The correlation with the Movement Imagery Questionnaire-Revised was high and the test showed stability in a one-week period. Gender invariance was demonstrated. CONCLUSIONS The positive psychometric properties of the Movement Imagery Questionnaire-3 in its Spanish version show that it can be used to measure imagery capabilities among a young and healthy population in both sexes.
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Affiliation(s)
- Sara Trapero-Asenjo
- Department of Nursing and Physiotherapy, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, Spain
| | - Tomás Gallego-Izquierdo
- Department of Nursing and Physiotherapy, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, Spain; Physiotherapy and Pain Group, Department of Nursing and Physiotherapy, Faculty of Medicine and Health Sciences, University of Alcalá, 28805, Alcalá de Henares, Spain
| | - Daniel Pecos-Martín
- Department of Nursing and Physiotherapy, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, Spain; Physiotherapy and Pain Group, Department of Nursing and Physiotherapy, Faculty of Medicine and Health Sciences, University of Alcalá, 28805, Alcalá de Henares, Spain.
| | - Susana Nunez-Nagy
- Department of Nursing and Physiotherapy, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, Spain
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Sorrentino G, Franza M, Zuber C, Blanke O, Serino A, Bassolino M. How ageing shapes body and space representations: A comparison study between healthy young and older adults. Cortex 2020; 136:56-76. [PMID: 33460913 DOI: 10.1016/j.cortex.2020.11.021] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 08/31/2020] [Accepted: 11/09/2020] [Indexed: 01/04/2023]
Abstract
To efficiently interact with the external world, the brain needs to represent the size of the involved body parts - body representations (BR) - and the space around the body in which the interactions with the environment take place - peripersonal space representation (PPS). BR and PPS are both highly flexible, being updated by the continuous flow of sensorimotor signals between the brain and the body, as observed for example after tool-use or immobilization. The progressive decline of sensorimotor abilities typically described in ageing could thus influence BR and PPS representations in the older adults. To explore this hypothesis, we compared BR and PPS in healthy young and older participants. By focusing on the upper limb, we adapted tasks previously used to evaluate BR and PPS plasticity, i.e., the body-landmarks localization task and audio-tactile interaction task, together with a new task targeting explicit BR (avatar adjustment task, AAT). Results show significantly higher distortions in the older rather than young participants in the perceived metric characteristic of the upper limbs. We found significant modifications in the implicit BR of the global shape (length and width) of both upper limbs, together with an underestimation in the arm length. Similar effects were also observed in the AAT task. Finally, both young and older adults showed equivalent multisensory facilitation in the space close to the hand, suggesting an intact PPS representation. Together, these findings demonstrated significant alterations of implicit and explicit BR in the older participants, probably associated with a less efficient contribution of bodily information typically subjected to age-related decline, whereas the comparable PPS representation in both groups could be supported by preserved multisensory abilities in older participants. These results provide novel empirical insight on how multiple representations of the body in space, subserving actions and perception, are shaped by the normal course of life.
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Affiliation(s)
- Giuliana Sorrentino
- Center for Neuroprosthetics, School of Life Science, Swiss Federal Institute of Technology (Ecole Polytechnique Fédérale de Lausanne), Campus Biotech, Geneva, Switzerland; Laboratory of Cognitive Neuroscience, Brain Mind Institute, School of Life Science, Swiss Federal Institute of Technology (Ecole Polytechnique Fédérale de Lausanne), Campus Biotech, Geneva, Switzerland; Center for Neuroprosthetics, School of Life Science, Swiss Federal Institute of Technology (Ecole Polytechnique Fédérale de Lausanne), Campus SUVA, Sion, Switzerland
| | - Matteo Franza
- Center for Neuroprosthetics, School of Life Science, Swiss Federal Institute of Technology (Ecole Polytechnique Fédérale de Lausanne), Campus Biotech, Geneva, Switzerland; Laboratory of Cognitive Neuroscience, Brain Mind Institute, School of Life Science, Swiss Federal Institute of Technology (Ecole Polytechnique Fédérale de Lausanne), Campus Biotech, Geneva, Switzerland; Center for Neuroprosthetics, School of Life Science, Swiss Federal Institute of Technology (Ecole Polytechnique Fédérale de Lausanne), Campus SUVA, Sion, Switzerland
| | - Charlène Zuber
- Center for Neuroprosthetics, School of Life Science, Swiss Federal Institute of Technology (Ecole Polytechnique Fédérale de Lausanne), Campus SUVA, Sion, Switzerland; Master of Science, University of Applied Sciences of Western, Switzerland
| | - Olaf Blanke
- Center for Neuroprosthetics, School of Life Science, Swiss Federal Institute of Technology (Ecole Polytechnique Fédérale de Lausanne), Campus Biotech, Geneva, Switzerland; Laboratory of Cognitive Neuroscience, Brain Mind Institute, School of Life Science, Swiss Federal Institute of Technology (Ecole Polytechnique Fédérale de Lausanne), Campus Biotech, Geneva, Switzerland; Center for Neuroprosthetics, School of Life Science, Swiss Federal Institute of Technology (Ecole Polytechnique Fédérale de Lausanne), Campus SUVA, Sion, Switzerland; Department of Neurology, University Hospital Geneva, Switzerland
| | - Andrea Serino
- Laboratory of Cognitive Neuroscience, Brain Mind Institute, School of Life Science, Swiss Federal Institute of Technology (Ecole Polytechnique Fédérale de Lausanne), Campus Biotech, Geneva, Switzerland; MySpace Lab, Department of Clinical Neuroscience, Centre Hospitalier Universitaire Vaudois (CHUV), Switzerland
| | - Michela Bassolino
- Laboratory of Cognitive Neuroscience, Brain Mind Institute, School of Life Science, Swiss Federal Institute of Technology (Ecole Polytechnique Fédérale de Lausanne), Campus Biotech, Geneva, Switzerland; Center for Neuroprosthetics, School of Life Science, Swiss Federal Institute of Technology (Ecole Polytechnique Fédérale de Lausanne), Campus SUVA, Sion, Switzerland; School of Health Sciences, HES-SO Valais-Wallis, Sion, Switzerland.
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Milosevic M, Marquez-Chin C, Masani K, Hirata M, Nomura T, Popovic MR, Nakazawa K. Why brain-controlled neuroprosthetics matter: mechanisms underlying electrical stimulation of muscles and nerves in rehabilitation. Biomed Eng Online 2020; 19:81. [PMID: 33148270 PMCID: PMC7641791 DOI: 10.1186/s12938-020-00824-w] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 10/10/2020] [Indexed: 12/11/2022] Open
Abstract
Delivering short trains of electric pulses to the muscles and nerves can elicit action potentials resulting in muscle contractions. When the stimulations are sequenced to generate functional movements, such as grasping or walking, the application is referred to as functional electrical stimulation (FES). Implications of the motor and sensory recruitment of muscles using FES go beyond simple contraction of muscles. Evidence suggests that FES can induce short- and long-term neurophysiological changes in the central nervous system by varying the stimulation parameters and delivery methods. By taking advantage of this, FES has been used to restore voluntary movement in individuals with neurological injuries with a technique called FES therapy (FEST). However, long-lasting cortical re-organization (neuroplasticity) depends on the ability to synchronize the descending (voluntary) commands and the successful execution of the intended task using a FES. Brain-computer interface (BCI) technologies offer a way to synchronize cortical commands and movements generated by FES, which can be advantageous for inducing neuroplasticity. Therefore, the aim of this review paper is to discuss the neurophysiological mechanisms of electrical stimulation of muscles and nerves and how BCI-controlled FES can be used in rehabilitation to improve motor function.
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Affiliation(s)
- Matija Milosevic
- Graduate School of Engineering Science, Department of Mechanical Science and Bioengineering, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan.
| | - Cesar Marquez-Chin
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON, M5S 3G9, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, 520 Sutherland Drive, Toronto, ON, M4G 3V9, Canada
- CRANIA, University Health Network & University of Toronto, 550 University Avenue, Toronto, ON, M5G 2A2, Canada
| | - Kei Masani
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON, M5S 3G9, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, 520 Sutherland Drive, Toronto, ON, M4G 3V9, Canada
- CRANIA, University Health Network & University of Toronto, 550 University Avenue, Toronto, ON, M5G 2A2, Canada
| | - Masayuki Hirata
- Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Taishin Nomura
- Graduate School of Engineering Science, Department of Mechanical Science and Bioengineering, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan
| | - Milos R Popovic
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON, M5S 3G9, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, 520 Sutherland Drive, Toronto, ON, M4G 3V9, Canada
- CRANIA, University Health Network & University of Toronto, 550 University Avenue, Toronto, ON, M5G 2A2, Canada
| | - Kimitaka Nakazawa
- Department of Life Sciences, Graduate School of Arts and Sciences, University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo, 153-8902, Japan
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Hernandez-Martin E, Marcano F, Modroño C, Janssen N, González-Mora JL. Diffuse optical tomography to measure functional changes during motor tasks: a motor imagery study. BIOMEDICAL OPTICS EXPRESS 2020; 11:6049-6067. [PMID: 33282474 PMCID: PMC7687968 DOI: 10.1364/boe.399907] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 09/12/2020] [Accepted: 09/16/2020] [Indexed: 05/03/2023]
Abstract
The present work shows the spatial reliability of the diffuse optical tomography (DOT) system in a group of healthy subjects during a motor imagery task. Prior to imagery task performance, the subjects executed a motor task based on the finger to thumb opposition for motor training, and to corroborate the DOT spatial localization during the motor execution. DOT technology and data treatment allows us to distinguish oxy- and deoxyhemoglobin at the cerebral gyri level unlike the cerebral activations provided by fMRI series that were processed using different approaches. Here we show the DOT reliability showing functional activations at the cerebral gyri level during motor execution and motor imagery, which provide subtler cerebral activations than the motor execution. These results will allow the use of the DOT system as a monitoring device in a brain computer interface.
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Affiliation(s)
- Estefania Hernandez-Martin
- Department of Basic Medical Science (Physiology), Faculty of Health Sciences, Medicine Section, Universidad de La Laguna 38071, Spain
| | - Francisco Marcano
- Department of Basic Medical Science (Physiology), Faculty of Health Sciences, Medicine Section, Universidad de La Laguna 38071, Spain
- Instituto de Tecnologías Biomédicas, Universidad de la Laguna, Spain
- Instituto de Neurociencias, Universidad de la Laguna, Spain
| | - Cristian Modroño
- Department of Basic Medical Science (Physiology), Faculty of Health Sciences, Medicine Section, Universidad de La Laguna 38071, Spain
- Instituto de Tecnologías Biomédicas, Universidad de la Laguna, Spain
- Instituto de Neurociencias, Universidad de la Laguna, Spain
| | - Niels Janssen
- Instituto de Tecnologías Biomédicas, Universidad de la Laguna, Spain
- Instituto de Neurociencias, Universidad de la Laguna, Spain
- Psychology Department, Universidad de La Laguna 38071, Spain
| | - Jose Luis González-Mora
- Department of Basic Medical Science (Physiology), Faculty of Health Sciences, Medicine Section, Universidad de La Laguna 38071, Spain
- Instituto de Tecnologías Biomédicas, Universidad de la Laguna, Spain
- Instituto de Neurociencias, Universidad de la Laguna, Spain
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Zapała D, Małkiewicz M, Francuz P, Kołodziej M, Majkowski A. Temperament Predictors of Motor Imagery Control in BCI. J PSYCHOPHYSIOL 2020. [DOI: 10.1027/0269-8803/a000252] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Abstract. The aim of this study was to verify if selected temperament traits may be useful as predictors of motor imagery brain-computer interface (BCI) performance. In our experiment, 40 BCI-naive subjects were instructed to imagine the movement of clenching his/her right or left hand, in accordance with the visual cue. The activity of sensorimotor rhythms (SMR) (8–30 Hz) was measured by electroencephalography (EEG) and transformed into the information transfer rate (ITR) after feature selection and classification. All subjects also completed a self-assessment questionnaire for the determination of their temperament profile, comprising the following traits: Briskness, Perseveration, Sensory Sensitivity, Emotional Reactivity, Endurance, and Activity. We found significant correlations between ITR performance and Endurance (EN) and Perseveration (PE) scores. This effect was also visible in a topography of SMR desynchronization patterns, in groups with different results in EN and PE scales. Finally, a predictive model of motor imagery BCI control based on temperament traits was proposed. We interpret this finding as empirical support for an influence of basic, relatively stable personality traits on BCI control via the performance of the motor imagery task. Moreover, the implication of these results on the design of future brain-computer interfaces was discussed.
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Affiliation(s)
- Dariusz Zapała
- Department of Experimental Psychology, The John Paul II Catholic University of Lublin, Poland
| | - Monika Małkiewicz
- Institute of Psychology, Cardinal Stefan Wyszynski University, Warsaw, Poland
| | - Piotr Francuz
- Department of Experimental Psychology, The John Paul II Catholic University of Lublin, Poland
| | - Marcin Kołodziej
- Institute of Theory of Electrical Engineering, Measurement and Information Systems, Warsaw University of Technology, Warsaw, Poland
| | - Andrzej Majkowski
- Institute of Theory of Electrical Engineering, Measurement and Information Systems, Warsaw University of Technology, Warsaw, Poland
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Collazos-Huertas DF, Álvarez-Meza AM, Acosta-Medina CD, Castaño-Duque GA, Castellanos-Dominguez G. CNN-based framework using spatial dropping for enhanced interpretation of neural activity in motor imagery classification. Brain Inform 2020; 7:8. [PMID: 32880784 PMCID: PMC7471227 DOI: 10.1186/s40708-020-00110-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 08/18/2020] [Indexed: 11/10/2022] Open
Abstract
Interpretation of brain activity responses using motor imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra- and inter-subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with an enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. We evaluate two labeled scenarios of MI tasks: bi-class and three-class. Obtained results in an MI database show that the thresholding strategy combined with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with a differentiated behavior of rhythms [Formula: see text] and [Formula: see text].
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Affiliation(s)
- D. F. Collazos-Huertas
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales, Colombia
| | - A. M. Álvarez-Meza
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales, Colombia
| | - C. D. Acosta-Medina
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales, Colombia
| | - G. A. Castaño-Duque
- Cultura de la Calidad en la Educación Research Group, Universidad Nacional de Colombia, Manizales, Colombia
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Stefano Filho CA, Costa TBS, S Uribe LF, Rodrigues PG, Soriano DC, Attux R, Castellano G. On the (in)efficacy of motor imagery training without feedback and event-related desynchronizations considerations. Biomed Phys Eng Express 2020; 6:035030. [PMID: 33438675 DOI: 10.1088/2057-1976/ab8992] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Motor imagery (MI) constitutes a recurrent strategy for signals generation in brain-computer interfaces (BCIs) - systems that aim to control external devices by directly associating brain responses to distinct commands. Although great improvement has been achieved in MI-BCIs performance over recent years, they still suffer from inter- and intra-subject variability issues. As an attempt to cope with this, some studies have suggested that MI training should aid users to appropriately modulate their response for BCI usage: generally, this training is performed based on the sensorimotor rhythms' modulation over the primary sensorimotor cortex (PMC), with the signal being feedbacked to the user. Nonetheless, recent studies have revisited the actual involvement of the PMC into MI, and little to no attention has been devoted to understanding the participation of other cortical areas into training protocols. Therefore, in this work, our aim was to analyze the response induced by hands MI of 10 healthy subjects in the form of event-related desynchronizations (ERDs) and to assess whether features from beyond the PMC might be useful for hands MI classification. We investigated how this response occurs for distinct frequency intervals between 7-30 Hz, and ex0plored changes in their evocation pattern across 12 MI training sessions without feedback. Overall, we found that ERD patterns occur differently for the frequencies encompassed by the μ and β bands, with its evocation being favored for the first band. Over time, the no-feedback approach was inefficient to aid in enhancing ERD evocation (EO). Moreover, to some extent, EO tends to decrease over blocks within a given run, and runs within an MI session, but remains stable within an MI block. We also found that the C3/C4 pair is not necessarily optimal for data classification, and both spectral and spatial subjects' specificities should be considered when designing training protocols.
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Affiliation(s)
- C A Stefano Filho
- Neurophysics Group, 'Gleb Wataghin' Physics Institute, University of Campinas (UNICAMP), Brazil. Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Brazil
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Electrocorticogram (ECoG) Is Highly Informative in Primate Visual Cortex. J Neurosci 2020; 40:2430-2444. [PMID: 32066581 DOI: 10.1523/jneurosci.1368-19.2020] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 02/08/2020] [Accepted: 02/10/2020] [Indexed: 12/21/2022] Open
Abstract
Neural signals recorded at different scales contain information about environment and behavior and have been used to control Brain Machine Interfaces with varying degrees of success. However, a direct comparison of their efficacy has not been possible due to different recording setups, tasks, species, etc. To address this, we implanted customized arrays having both microelectrodes and electrocorticogram (ECoG) electrodes in the primary visual cortex of 2 female macaque monkeys, and also recorded electroencephalogram (EEG), while they viewed a variety of naturalistic images and parametric gratings. Surprisingly, ECoG had higher information and decodability than all other signals. Combining a few ECoG electrodes allowed more accurate decoding than combining a much larger number of microelectrodes. Control analyses showed that higher decoding accuracy of ECoG compared with local field potential was not because of differences in low-level visual features captured by them but instead because of larger spatial summation of the ECoG. Information was high in the 30-80 Hz range and at lower frequencies. Information in different frequencies and scales was nonredundant. These results have strong implications for Brain Machine Interface applications and for study of population representation of visual stimuli.SIGNIFICANCE STATEMENT Electrophysiological signals captured across scales by different recording electrodes are regularly used for Brain Machine Interfaces, but the information content varies due to electrode size and location. A systematic comparison of their efficiency for Brain Machine Interfaces is important but technically challenging. Here, we recorded simultaneous signals across four scales: spikes, local field potential, electrocorticogram (ECoG), and EEG, and compared their information and decoding accuracy for a large variety of naturalistic stimuli. We found that ECoGs were highly informative and outperformed other signals in information content and decoding accuracy.
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The effects of handedness on sensorimotor rhythm desynchronization and motor-imagery BCI control. Sci Rep 2020; 10:2087. [PMID: 32034277 PMCID: PMC7005877 DOI: 10.1038/s41598-020-59222-w] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 01/27/2020] [Indexed: 11/17/2022] Open
Abstract
Brain–computer interfaces (BCIs) allow control of various applications or external devices solely by brain activity, e.g., measured by electroencephalography during motor imagery. Many users are unable to modulate their brain activity sufficiently in order to control a BCI. Most of the studies have been focusing on improving the accuracy of BCI control through advances in signal processing and BCI protocol modification. However, some research suggests that motor skills and physiological factors may affect BCI performance as well. Previous studies have indicated that there is differential lateralization of hand movements’ neural representation in right- and left-handed individuals. However, the effects of handedness on sensorimotor rhythm (SMR) distribution and BCI control have not been investigated in detail yet. Our study aims to fill this gap, by comparing the SMR patterns during motor imagery and real-feedback BCI control in right- (N = 20) and left-handers (N = 20). The results of our study show that the lateralization of SMR during a motor imagery task differs according to handedness. Left-handers present lower accuracy during BCI performance (single session) and weaker SMR suppression in the alpha band (8–13 Hz) during mental simulation of left-hand movements. Consequently, to improve BCI control, the user’s training should take into account individual differences in hand dominance.
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Wang K, Xu M, Wang Y, Zhang S, Chen L, Ming D. Enhance decoding of pre-movement EEG patterns for brain-computer interfaces. J Neural Eng 2020; 17:016033. [PMID: 31747642 DOI: 10.1088/1741-2552/ab598f] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
OBJECTIVE In recent years, brain-computer interface (BCI) systems based on electroencephalography (EEG) have developed rapidly. However, the decoding of voluntary finger pre-movements from EEG is still a challenge for BCIs. This study aimed to analyze the pre-movement EEG features in time and frequency domains and design an efficient method to decode the movement-related patterns. APPROACH In this study, we first investigated the EEG features induced by the intention of left and right finger movements. Specifically, the movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were extracted using discriminative canonical pattern matching (DCPM) and common spatial patterns (CSP), respectively. Then, the two types of features were classified by two fisher discriminant analysis (FDA) classifiers, respectively. Their decision values were further assembled to facilitate the classification. To verify the validity of the proposed method, a private dataset containing 12 subjects and a public dataset from BCI competition II were used for estimating the classification accuracy. MAIN RESULTS As a result, for the private dataset, the combination of DCPM and CSP achieved an average accuracy of 80.96%, which was 5.08% higher than the single DCPM method (p < 0.01) and 10.23% higher than the single CSP method (p < 0.01). Notably, the highest accuracy could achieve 91.5% for the combination method. The test accuracy of dataset IV of BCI competition II was 90%, which was equal to the best result in the existing literature. SIGNIFICANCE The results demonstrate the MRCP and ERD features of pre-movements contain significantly discriminative information, which are complementary to each other, and thereby could be well recognized by the proposed combination method of DCPM and CSP. Therefore, this study provides a promising approach for the decoding of pre-movement EEG patterns, which is significant for the development of BCIs.
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Affiliation(s)
- Kun Wang
- Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China. Contributed equally to this work
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Spychala N, Debener S, Bongartz E, Müller HHO, Thorne JD, Philipsen A, Braun N. Exploring Self-Paced Embodiable Neurofeedback for Post-stroke Motor Rehabilitation. Front Hum Neurosci 2020; 13:461. [PMID: 32038198 PMCID: PMC6984194 DOI: 10.3389/fnhum.2019.00461] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 12/16/2019] [Indexed: 12/14/2022] Open
Abstract
Neurofeedback-guided motor-imagery training (NF-MIT) has been proposed as a promising intervention following upper limb motor impairment. In this intervention, paretic stroke patients receive online feedback about their brain activity while conducting a motor-imagery (MI) task with the paretic limb. Typically, the feedback provided in NF-MIT protocols is an abstract visual signal based on a fixed trial. Here we developed a self-paced NF-MIT paradigm with an embodiable feedback signal (EFS), which was designed to resemble the content of the mental act as closely as possible. To this end, the feedback was delivered via an embodiable, anthropomorphic robotic hand (RH), which was integrated into a closed-looped EEG-based brain-computer interface (BCI). Whenever the BCI identified a new instance of a hand-flexion or hand-extension imagination by the participant, the RH carried out the corresponding movement with minimum delay. Nine stroke patients and nine healthy participants were instructed to control RH movements as accurately as possible, using mental activity alone. We evaluated the general feasibility of our paradigm on electrophysiological, subjective and performance levels. Regarding electrophysiological measures, individuals showed the predicted event-related desynchronization (ERD) patterns over sensorimotor brain areas. On the subjective level, we found that most individuals integrated the RH into their body scheme. With respect to RH control, none of our participants achieved a high level of control, but most managed to control the RH actions to some degree. Importantly, patients and controls achieved similar performance levels. The results support the view that self-paced embodiable NF-MIT is feasible for stroke patients and can complement classical NF-MIT.
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Affiliation(s)
- Nadine Spychala
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| | - Stefan Debener
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| | - Edith Bongartz
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| | - Helge H O Müller
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Jeremy D Thorne
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| | - Alexandra Philipsen
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Niclas Braun
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany.,Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
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Lotte F, Jeunet C, Chavarriaga R, Bougrain L, Thompson DE, Scherer R, Mowla MR, Kübler A, Grosse-Wentrup M, Dijkstra K, Dayan N. Turning negative into positives! Exploiting ‘negative’ results in Brain–Machine Interface (BMI) research. BRAIN-COMPUTER INTERFACES 2020. [DOI: 10.1080/2326263x.2019.1697143] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Fabien Lotte
- Inria, LaBRI, CNRS/University of Bordeaux/Bordeaux INP, Bordeaux, France
| | - Camille Jeunet
- CLLE Lab, CNRS, University of Toulouse Jean Jaurès, Toulouse, France
| | - Ricardo Chavarriaga
- Brain-Machine Interface, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | | | - Dave E. Thompson
- Brain and Body Sensing Laboratory, Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA
| | - Reinhold Scherer
- Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Md Rakibul Mowla
- Brain and Body Sensing Laboratory, Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA
| | - Andrea Kübler
- Institute of Psychology, University of Würzburg, Wurzburg, Germany
| | - Moritz Grosse-Wentrup
- Research Group Neuroinformatics, Faculty of Computer Science, University of Vienna, Vienna, Austria
| | - Karen Dijkstra
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
| | - Natalie Dayan
- Intelligent Systems Research Center, Ulster University, Londonderry, Northern Ireland
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Igasaki T, Takemoto J, Sakamoto K. Relationship Between Kinesthetic/Visual Motor Imagery Difficulty and Event-Related Desynchronization/Synchronization. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:1911-1914. [PMID: 30440771 DOI: 10.1109/embc.2018.8512673] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Motor imagery (MI) is divided into two types: kinesthetic (KMI) and visual (VMI). To estimate the MI that an examinee performs, event-related desynchronization (ERD) or event-related synchronization (ERS) is used to characterize KMI or VMI via electroencephalogram (EEG). However, no definitive method using ERD/ERS via EEG has been established yet to estimate the type of MI performed. This is because the MI performed by the examinee is not always the same as that instructed by the examiner. One of the reasons for this mismatch is the difficulty of MI, especially KMI. However, almost no reported studies have considered MI difficulty to estimate MI type. Therefore, in this study, we examined the relationship between MI difficulty and the ERD/ERS pattern corresponding to the type of MI in the case of single flexion of the right index finger (SFRIF). The results showed that for a subject who felt MI was less difficult, the α-band ERD value (αERD) at the electrode of the occipital area (O1 or O2 site) of the KMI instruction was significantly smaller than that of the VMI instruction. On the contrary, for a subject who felt MI was very difficult, αERD at the O1 or O2 site on the KMI instruction was similar to that of the VMI instruction. In addition, for the subject who felt MI was easy, the αERDs at the O1 or O2 site on the KMI and VMI instructions were similar to those on the movement execution (ME) and movement observation (MO) instructions, respectively. Therefore, in the case of SFRIF, it was suggested that MI difficulty could be estimated by ERD/ERS patterns in the occipital area. This was supported by referring to the ME and MO ERD/ERS patterns in the occipital area.
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Lee WH, Kim E, Seo HG, Oh BM, Nam HS, Kim YJ, Lee HH, Kang MG, Kim S, Bang MS. Target-oriented motor imagery for grasping action: different characteristics of brain activation between kinesthetic and visual imagery. Sci Rep 2019; 9:12770. [PMID: 31484971 PMCID: PMC6726765 DOI: 10.1038/s41598-019-49254-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 08/21/2019] [Indexed: 02/05/2023] Open
Abstract
Motor imagery (MI) for target-oriented movements, which is a basis for functional activities of daily living, can be more appropriate than non-target-oriented MI as tasks to promote motor recovery or brain-computer interface (BCI) applications. This study aimed to explore different characteristics of brain activation among target-oriented kinesthetic imagery (KI) and visual imagery (VI) in the first-person (VI-1) and third-person (VI-3) perspectives. Eighteen healthy volunteers were evaluated for MI ability, trained for the three types of target-oriented MIs, and scanned using 3 T functional magnetic resonance imaging (fMRI) under MI and perceptual control conditions, presented in a block design. Post-experimental questionnaires were administered after fMRI. Common brain regions activated during the three types of MI were the left premotor area and inferior parietal lobule, irrespective of the MI modalities or perspectives. Contrast analyses showed significantly increased brain activation only in the contrast of KI versus VI-1 and KI versus VI-3 for considerably extensive brain regions, including the supplementary motor area and insula. Neural activity in the orbitofrontal cortex and cerebellum during VI-1 and KI was significantly correlated with MI ability measured by mental chronometry and a self-reported questionnaire, respectively. These results can provide a basis in developing MI-based protocols for neurorehabilitation to improve motor recovery and BCI training in severely paralyzed individuals.
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Affiliation(s)
- Woo Hyung Lee
- Department of Biomedical Engineering, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Eunkyung Kim
- Department of Rehabilitation Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Han Gil Seo
- Department of Rehabilitation Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Byung-Mo Oh
- Department of Rehabilitation Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Hyung Seok Nam
- Department of Rehabilitation Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Yoon Jae Kim
- Department of Biomedical Engineering, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Hyun Haeng Lee
- Department of Rehabilitation Medicine, Konkuk University Hospital, 120-1 Hwayang-dong, Gwangjin-gu, Seoul, 05030, Republic of Korea
| | - Min-Gu Kang
- Department of Rehabilitation Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Sungwan Kim
- Department of Biomedical Engineering, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
- Institute of Bioengineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea.
| | - Moon Suk Bang
- Department of Rehabilitation Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
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Albasri A, Abdali-Mohammadi F, Fathi A. EEG electrode selection for person identification thru a genetic-algorithm method. J Med Syst 2019; 43:297. [PMID: 31350595 DOI: 10.1007/s10916-019-1364-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 05/30/2019] [Indexed: 11/24/2022]
Abstract
New biometric identification techniques are continually being developed to meet various applications. Electroencephalography (EEG) signals may provide a reasonable option for this type of identification due its unique features that overcome the lacks of other common methods. Currently, however, the processing load for such signals requires considerable time and labor. New methods and algorithms have attempted to reduce EEG processing time, including a reduction of the number of electrodes and segmenting the EEG data into its typical frequency bands. This work complements other efforts by proposing a genetic algorithm to reduce the number of necessary electrodes for measurements by EEG devices. Using a public EEG dataset of 109 subjects who underwent relaxation with eye-open and eye-closed stimuli, we aimed to determine the minimum set of electrodes required for optimum identification accuracy in each EEG sub-band of both stimuli. The results were encouraging and it was possible to accurately identify a subject using about 10 out of 64 electrodes. Moreover, higher frequency bands required a fewer number of electrodes for identification compared with lower frequency bands.
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Affiliation(s)
- Ahmed Albasri
- Department of Computer and Information Technology, Faculty of Engineering, Razi University, Kermanshah, Iran
| | - Fardin Abdali-Mohammadi
- Department of Computer and Information Technology, Faculty of Engineering, Razi University, Kermanshah, Iran.
| | - Abdolhossein Fathi
- Department of Computer and Information Technology, Faculty of Engineering, Razi University, Kermanshah, Iran
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Zapała D, Francuz P, Zapała E, Kopiś N, Wierzgała P, Augustynowicz P, Majkowski A, Kołodziej M. The Impact of Different Visual Feedbacks in User Training on Motor Imagery Control in BCI. Appl Psychophysiol Biofeedback 2019; 43:23-35. [PMID: 29075937 PMCID: PMC5869881 DOI: 10.1007/s10484-017-9383-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
The challenges of research into brain–computer interfaces (BCI) include significant individual differences in learning pace and in the effective operation of BCI devices. The use of neurofeedback training is a popular method of improving the effectiveness BCI operation. The purpose of the present study was to determine to what extent it is possible to improve the effectiveness of operation of sensorimotor rhythm-based brain–computer interfaces (SMR-BCI) by supplementing user training with elements modifying the characteristics of visual feedback. Four experimental groups had training designed to reinforce BCI control by: visual feedback in the form of dummy faces expressing emotions (Group 1); flashing the principal elements of visual feedback (Group 2) and giving both visual feedbacks in one condition (Group 3). The fourth group participated in training with no modifications (Group 4). Training consisted of a series of trials where the subjects directed a ball into a basket located to the right or left side of the screen. In Group 1 a schematic image a face, placed on the controlled object, showed various emotions, depending on the accuracy of control. In Group 2, the cue and targets were flashed with different frequency (4 Hz) than the remaining elements visible on the monitor. Both modifications were also used simultaneously in Group 3. SMR activity during the task was recorded before and after the training. In Group 3 there was a significant improvement in SMR control, compared to subjects in Group 2 and 4 (control). Differences between subjects in Groups 1, 2 and 4 (control) were insignificant. This means that relatively small changes in the training procedure may significantly impact the effectiveness of BCI control. Analysis of behavioural data acquired from all participants at training showed greater effectiveness in directing the object towards the right side of the screen. Subjects with the greatest improvement in SMR control showed a significantly lower difference in the accuracy of rightward and leftward movement than others.
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Affiliation(s)
- Dariusz Zapała
- Department of Experimental Psychology, The John Paul II Catholic University of Lublin, Lublin, Poland.
| | - Piotr Francuz
- Department of Experimental Psychology, The John Paul II Catholic University of Lublin, Lublin, Poland
| | - Ewelina Zapała
- Department of Experimental Psychology, The John Paul II Catholic University of Lublin, Lublin, Poland
| | - Natalia Kopiś
- Department of Experimental Psychology, The John Paul II Catholic University of Lublin, Lublin, Poland
| | - Piotr Wierzgała
- Institute of Computer Science, Maria Curie-Sklodowska University, Lublin, Poland
| | - Paweł Augustynowicz
- Department of Experimental Psychology, The John Paul II Catholic University of Lublin, Lublin, Poland
- Institute of Economics and Finance, Maria Curie-Skłodowska University, Lublin, Poland
| | - Andrzej Majkowski
- Institute of Theory of Electrical Engineering, Measurement and Information Systems, Warsaw University of Technology, Warsaw, Poland
| | - Marcin Kołodziej
- Institute of Theory of Electrical Engineering, Measurement and Information Systems, Warsaw University of Technology, Warsaw, Poland
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Padfield N, Zabalza J, Zhao H, Masero V, Ren J. EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges. SENSORS (BASEL, SWITZERLAND) 2019; 19:E1423. [PMID: 30909489 PMCID: PMC6471241 DOI: 10.3390/s19061423] [Citation(s) in RCA: 186] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 03/10/2019] [Accepted: 03/19/2019] [Indexed: 12/11/2022]
Abstract
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.
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Affiliation(s)
- Natasha Padfield
- Centre for Signal and Image Processing, University of Strathclyde, Glasgow G1 1XW, UK.
| | - Jaime Zabalza
- Centre for Signal and Image Processing, University of Strathclyde, Glasgow G1 1XW, UK.
| | - Huimin Zhao
- School of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
- The Guangzhou Key Laboratory of Digital Content Processing and Security Technologies, Guangzhou 510665, China.
| | - Valentin Masero
- Department of Computer Systems and Telematics Engineering, Universidad de Extremadura, 06007 Badajoz, Spain.
| | - Jinchang Ren
- Centre for Signal and Image Processing, University of Strathclyde, Glasgow G1 1XW, UK.
- School of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
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Rimbert S, Gayraud N, Bougrain L, Clerc M, Fleck S. Can a Subjective Questionnaire Be Used as Brain-Computer Interface Performance Predictor? Front Hum Neurosci 2019; 12:529. [PMID: 30728772 PMCID: PMC6352609 DOI: 10.3389/fnhum.2018.00529] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Accepted: 12/13/2018] [Indexed: 11/13/2022] Open
Abstract
Predicting a subject's ability to use a Brain Computer Interface (BCI) is one of the major issues in the BCI domain. Relevant applications of forecasting BCI performance include the ability to adapt the BCI to the needs and expectations of the user, assessing the efficiency of BCI use in stroke rehabilitation, and finally, homogenizing a research population. A limited number of recent studies have proposed the use of subjective questionnaires, such as the Motor Imagery Questionnaire Revised-Second Edition (MIQ-RS). However, further research is necessary to confirm the effectiveness of this type of subjective questionnaire as a BCI performance estimation tool. In this study we aim to answer the following questions: can the MIQ-RS be used to estimate the performance of an MI-based BCI? If not, can we identify different markers that could be used as performance estimators? To answer these questions, we recorded EEG signals from 35 healthy volunteers during BCI use. The subjects had previously completed the MIQ-RS questionnaire. We conducted an offline analysis to assess the correlation between the questionnaire scores related to Kinesthetic and Motor imagery tasks and the performances of four classification methods. Our results showed no significant correlation between BCI performance and the MIQ-RS scores. However, we reveal that BCI performance is correlated to habits and frequency of practicing manual activities.
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Affiliation(s)
| | - Nathalie Gayraud
- Université Côte d'Azur, Inria, Sophia-Antipolis Mditerrannée, Athena Team, Valbonne, France
| | - Laurent Bougrain
- Université de Lorraine, Inria, LORIA, Neurosys Team, Nancy, France
| | - Maureen Clerc
- Université Côte d'Azur, Inria, Sophia-Antipolis Mditerrannée, Athena Team, Valbonne, France
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