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Canny E, Vansteensel MJ, van der Salm SMA, Müller-Putz GR, Berezutskaya J. Boosting brain-computer interfaces with functional electrical stimulation: potential applications in people with locked-in syndrome. J Neuroeng Rehabil 2023; 20:157. [PMID: 37980536 PMCID: PMC10656959 DOI: 10.1186/s12984-023-01272-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 10/23/2023] [Indexed: 11/20/2023] Open
Abstract
Individuals with a locked-in state live with severe whole-body paralysis that limits their ability to communicate with family and loved ones. Recent advances in brain-computer interface (BCI) technology have presented a potential alternative for these people to communicate by detecting neural activity associated with attempted hand or speech movements and translating the decoded intended movements to a control signal for a computer. A technique that could potentially enrich the communication capacity of BCIs is functional electrical stimulation (FES) of paralyzed limbs and face to restore body and facial movements of paralyzed individuals, allowing to add body language and facial expression to communication BCI utterances. Here, we review the current state of the art of existing BCI and FES work in people with paralysis of body and face and propose that a combined BCI-FES approach, which has already proved successful in several applications in stroke and spinal cord injury, can provide a novel promising mode of communication for locked-in individuals.
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Affiliation(s)
- Evan Canny
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Mariska J Vansteensel
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Sandra M A van der Salm
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Gernot R Müller-Putz
- Institute of Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Graz, Austria
| | - Julia Berezutskaya
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
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Pulferer HS, Ásgeirsdóttir B, Mondini V, Sburlea AI, Müller-Putz GR. Continuous 2D trajectory decoding from attempted movement: across-session performance in able-bodied and feasibility in a spinal cord injured participant. J Neural Eng 2022; 19. [PMID: 35443233 DOI: 10.1088/1741-2552/ac689f] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 04/19/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE In people with a cervical spinal cord injury (SCI) or degenerative diseases leading to limited motor function, restoration of upper limb movement has been a goal of the brain-computer interface (BCI) field for decades. Recently, research from our group investigated non-invasive and real-time decoding of continuous movement in able-bodied participants from low-frequency brain signals during a target-tracking task. To advance our setup towards motor-impaired end users, we consequently chose a new paradigm based on attempted movement. APPROACH Here, we present the results of two studies. During the first study, data of ten able-bodied participants completing a target-tracking/shape-tracing task on-screen were investigated in terms of improvements in decoding performance due to user training. In a second study, a spinal cord injured participant underwent the same tasks. To investigate the merit of employing attempted movement in end users with SCI, data of the spinal cord injured participant were recorded twice; once within an observation only condition, and once while simultaneously attempting movement. MAIN RESULTS We observed mean correlation well above chance level for continuous motor decoding based on attempted movement in able-bodied participants. No global improvement over three sessions, both in sensor and source space, could be observed across all participants and movement parameters. In the participant with SCI, decoding performance well above chance was found. SIGNIFICANCE No presence of a learning effect in continuous attempted movement decoding in able-bodied participants could be observed. In contrast, non-significantly varying decoding patterns may promote the use of source space decoding in terms of generalized decoders utilizing transfer learning. Furthermore, above-chance correlations for attempted movement decoding ranging between those of observation only and executed movement were seen in one spinal cord injured participant, suggesting attempted movement decoding as a possible link between feasibility studies in able-bodied and actual applications in motor impaired end users.
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Affiliation(s)
| | | | - Valeria Mondini
- Institute of Neural Engineering, Graz University of Technology, Stremayrgasse 16/IV, Graz, 8010, AUSTRIA
| | - Andreea Ioana Sburlea
- Institute of Neural Engineering, Technische Universitat Graz, Stremayrgasse 16/IV, 8010 Graz, Austria, Graz, 8010, AUSTRIA
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Srisrisawang N, Müller-Putz GR. Applying Dimensionality Reduction Techniques in Source-Space Electroencephalography via Template and Magnetic Resonance Imaging-Derived Head Models to Continuously Decode Hand Trajectories. Front Hum Neurosci 2022; 16:830221. [PMID: 35399364 PMCID: PMC8988304 DOI: 10.3389/fnhum.2022.830221] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 02/28/2022] [Indexed: 11/13/2022] Open
Abstract
Several studies showed evidence supporting the possibility of hand trajectory decoding from low-frequency electroencephalography (EEG). However, the decoding in the source space via source localization is scarcely investigated. In this study, we tried to tackle the problem of collinearity due to the higher number of signals in the source space by two folds: first, we selected signals in predefined regions of interest (ROIs); second, we applied dimensionality reduction techniques to each ROI. The dimensionality reduction techniques were computing the mean (Mean), principal component analysis (PCA), and locality preserving projections (LPP). We also investigated the effect of decoding between utilizing a template head model and a subject-specific head model during the source localization. The results indicated that applying source-space decoding with PCA yielded slightly higher correlations and signal-to-noise (SNR) ratios than the sensor-space approach. We also observed slightly higher correlations and SNRs when applying the subject-specific head model than the template head model. However, the statistical tests revealed no significant differences between the source-space and sensor-space approaches and no significant differences between subject-specific and template head models. The decoder with Mean and PCA utilizes information mainly from precuneus and cuneus to decode the velocity kinematics similarly in the subject-specific and template head models.
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Affiliation(s)
| | - Gernot R. Müller-Putz
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
- BioTechMed Graz, Graz, Austria
- *Correspondence: Gernot R. Müller-Putz,
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Müller-Putz GR, Kobler RJ, Pereira J, Lopes-Dias C, Hehenberger L, Mondini V, Martínez-Cagigal V, Srisrisawang N, Pulferer H, Batistić L, Sburlea AI. Feel Your Reach: An EEG-Based Framework to Continuously Detect Goal-Directed Movements and Error Processing to Gate Kinesthetic Feedback Informed Artificial Arm Control. Front Hum Neurosci 2022; 16:841312. [PMID: 35360289 PMCID: PMC8961864 DOI: 10.3389/fnhum.2022.841312] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 02/16/2022] [Indexed: 11/13/2022] Open
Abstract
Establishing the basic knowledge, methodology, and technology for a framework for the continuous decoding of hand/arm movement intention was the aim of the ERC-funded project “Feel Your Reach”. In this work, we review the studies and methods we performed and implemented in the last 6 years, which build the basis for enabling severely paralyzed people to non-invasively control a robotic arm in real-time from electroencephalogram (EEG). In detail, we investigated goal-directed movement detection, decoding of executed and attempted movement trajectories, grasping correlates, error processing, and kinesthetic feedback. Although we have tested some of our approaches already with the target populations, we still need to transfer the “Feel Your Reach” framework to people with cervical spinal cord injury and evaluate the decoders’ performance while participants attempt to perform upper-limb movements. While on the one hand, we made major progress towards this ambitious goal, we also critically discuss current limitations.
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Affiliation(s)
- Gernot R. Müller-Putz
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
- BioTechMed, Graz, Austria
- *Correspondence: Gernot R. Müller-Putz
| | - Reinmar J. Kobler
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
- RIKEN Center for Advanced Intelligence Project, Kyoto, Japan
| | - Joana Pereira
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
- Brain-State Decoding Lab, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany
- Stereotaxy and Functional Neurosurgery Department, Uniklinik Freiburg, Freiburg, Germany
| | - Catarina Lopes-Dias
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Lea Hehenberger
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Valeria Mondini
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Víctor Martínez-Cagigal
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Valladolid, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Valladolid, Spain
| | | | - Hannah Pulferer
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Luka Batistić
- Faculty of Engineering, Department of Computer Engineering, University of Rijeka, Rijeka, Croatia
| | - Andreea I. Sburlea
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
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5
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Lopes-Dias C, Sburlea AI, Breitegger K, Wyss D, Drescher H, Wildburger R, Müller-Putz GR. Online asynchronous detection of error-related potentials in participants with a spinal cord injury using a generic classifier. J Neural Eng 2021; 18:046022. [PMID: 33779576 DOI: 10.1088/1741-2552/abd1eb] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
For brain-computer interface (BCI) users, the awareness of an error is associated with a cortical signature known as an error-related potential (ErrP). The incorporation of ErrP detection into BCIs can improve their performance. OBJECTIVE This work has three main aims. First, we investigate whether an ErrP classifier is transferable from able-bodied participants to participants with a spinal cord injury (SCI). Second, we test this generic ErrP classifier with SCI and control participants, in an online experiment without offline calibration. Third, we investigate the morphology of ErrPs in both groups of participants. APPROACH We used previously recorded electroencephalographic data from able-bodied participants to train an ErrP classifier. We tested the classifier asynchronously, in an online experiment with 16 new participants: 8 participants with SCI and 8 able-bodied control participants. The experiment had no offline calibration and participants received feedback regarding the ErrP detections from the start. To increase the fluidity of the experiment, feedback regarding false positive ErrP detections was not presented to the participants, but these detections were taken into account in the evaluation of the classifier. The generic classifier was not trained with the user's brain signals. However, its performance was optimized during the online experiment by the use of personalized decision thresholds. The classifier's performance was evaluated using trial-based metrics, which considered the asynchronous detection of ErrPs during the entire trial's duration. MAIN RESULTS Participants with SCI presented a non-homogenous ErrP morphology, and four of them did not present clear ErrP signals. The generic classifier performed better than chance in participants with clear ErrP signals, independently of the SCI (11 out of 16 participants). Three out of the five participants that obtained chance level results with the generic classifier would have not benefitted from the use of a personalized classifier. SIGNIFICANCE This work shows the feasibility of transferring an ErrP classifier from able-bodied participants to participants with SCI, for asynchronous detection of ErrPs in an online experiment without offline calibration, which provided immediate feedback to the users.
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Affiliation(s)
- Catarina Lopes-Dias
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
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6
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Kobler RJ, Sburlea AI, Mondini V, Hirata M, Müller-Putz GR. Distance- and speed-informed kinematics decoding improves M/EEG based upper-limb movement decoder accuracy. J Neural Eng 2020; 17:056027. [PMID: 33146148 DOI: 10.1088/1741-2552/abb3b3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE One of the main goals in brain-computer interface (BCI) research is the replacement or restoration of lost function in individuals with paralysis. One line of research investigates the inference of movement kinematics from brain activity during different volitional states. A growing number of electroencephalography (EEG) and magnetoencephalography (MEG) studies suggest that information about directional (e.g. velocity) and nondirectional (e.g. speed) movement kinematics is accessible noninvasively. We sought to assess if the neural information associated with both types of kinematics can be combined to improve the decoding accuracy. APPROACH In an offline analysis, we reanalyzed the data of two previous experiments containing the recordings of 34 healthy participants (15 EEG, 19 MEG). We decoded 2D movement trajectories from low-frequency M/EEG signals in executed and observed tracking movements, and compared the accuracy of an unscented Kalman filter (UKF) that explicitly modeled the nonlinear relation between directional and nondirectional kinematics to the accuracies of linear Kalman (KF) and Wiener filters which did not combine both types of kinematics. MAIN RESULTS At the group level, posterior-parietal and parieto-occipital (executed and observed movements) and sensorimotor areas (executed movements) encoded kinematic information. Correlations between the recorded position and velocity trajectories and the UKF decoded ones were on average 0.49 during executed and 0.36 during observed movements. Compared to the other filters, the UKF could achieve the best trade-off between maximizing the signal to noise ratio and minimizing the amplitude mismatch between the recorded and decoded trajectories. SIGNIFICANCE We present direct evidence that directional and nondirectional kinematic information is simultaneously detectable in low-frequency M/EEG signals. Moreover, combining directional and nondirectional kinematic information significantly improves the decoding accuracy upon a linear KF.
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Affiliation(s)
- Reinmar J Kobler
- Institute of Neural Engineering, Graz University of Technology, Graz 8010, Styria, Austria
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Mondini V, Kobler RJ, Sburlea AI, Müller-Putz GR. Continuous low-frequency EEG decoding of arm movement for closed-loop, natural control of a robotic arm. J Neural Eng 2020; 17:046031. [DOI: 10.1088/1741-2552/aba6f7] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Abiri R, Borhani S, Kilmarx J, Esterwood C, Jiang Y, Zhao X. A Usability Study of Low-cost Wireless Brain-Computer Interface for Cursor Control Using Online Linear Model. IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 2020; 50:287-297. [PMID: 33777542 PMCID: PMC7990128 DOI: 10.1109/thms.2020.2983848] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Computer cursor control using electroencephalogram (EEG) signals is a common and well-studied brain-computer interface (BCI). The emphasis of the literature has been primarily on evaluation of the objective measures of assistive BCIs such as accuracy of the neural decoder whereas the subjective measures such as user's satisfaction play an essential role for the overall success of a BCI. As far as we know, the BCI literature lacks a comprehensive evaluation of the usability of the mind-controlled computer cursor in terms of decoder efficiency (accuracy), user experience, and relevant confounding variables concerning the platform for the public use. To fill this gap, we conducted a two-dimensional EEG-based cursor control experiment among 28 healthy participants. The computer cursor velocity was controlled by the imagery of hand movement using a paradigm presented in the literature named imagined body kinematics (IBK) with a low-cost wireless EEG headset. We evaluated the usability of the platform for different objective and subjective measures while we investigated the extent to which the training phase may influence the ultimate BCI outcome. We conducted pre- and post- BCI experiment interview questionnaires to evaluate the usability. Analyzing the questionnaires and the testing phase outcome shows a positive correlation between the individuals' ability of visualization and their level of mental controllability of the cursor. Despite individual differences, analyzing training data shows the significance of electrooculogram (EOG) on the predictability of the linear model. The results of this work may provide useful insights towards designing a personalized user-centered assistive BCI.
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Affiliation(s)
- Reza Abiri
- Dept. of Neurology at University of California, San Francisco/Berkeley and Dept. of Mechanical, Aerospace, and Biomedical Engineering at the University of Tennessee, Knoxville
| | - Soheil Borhani
- Department of Mechanical, Aerospace, and Biomedical Engineering, The University of Tennessee, Knoxville, TN 37996 USA
| | - Justin Kilmarx
- Department of Mechanical, Aerospace, and Biomedical Engineering, The University of Tennessee, Knoxville, TN 37996 USA
| | - Connor Esterwood
- College Communication and Information at the University of Tennessee, Knoxville, TN, USA
| | - Yang Jiang
- Department of Behavioral Science, College of Medicine, at University of Kentucky, Lexington KY, USA
| | - Xiaopeng Zhao
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996 USA
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Sosnik R, Ben Zur O. Reconstruction of hand, elbow and shoulder actual and imagined trajectories in 3D space using EEG slow cortical potentials. J Neural Eng 2020; 17:016065. [DOI: 10.1088/1741-2552/ab59a7] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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10
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Ofner P, Schwarz A, Pereira J, Wyss D, Wildburger R, Müller-Putz GR. Attempted Arm and Hand Movements can be Decoded from Low-Frequency EEG from Persons with Spinal Cord Injury. Sci Rep 2019; 9:7134. [PMID: 31073142 PMCID: PMC6509331 DOI: 10.1038/s41598-019-43594-9] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 04/26/2019] [Indexed: 01/08/2023] Open
Abstract
We show that persons with spinal cord injury (SCI) retain decodable neural correlates of attempted arm and hand movements. We investigated hand open, palmar grasp, lateral grasp, pronation, and supination in 10 persons with cervical SCI. Discriminative movement information was provided by the time-domain of low-frequency electroencephalography (EEG) signals. Based on these signals, we obtained a maximum average classification accuracy of 45% (chance level was 20%) with respect to the five investigated classes. Pattern analysis indicates central motor areas as the origin of the discriminative signals. Furthermore, we introduce a proof-of-concept to classify movement attempts online in a closed loop, and tested it on a person with cervical SCI. We achieved here a modest classification performance of 68.4% with respect to palmar grasp vs hand open (chance level 50%).
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Affiliation(s)
- Patrick Ofner
- Graz University of Technology, Institute of Neural Engineering, BCI-Lab, Graz, Austria
| | - Andreas Schwarz
- Graz University of Technology, Institute of Neural Engineering, BCI-Lab, Graz, Austria
| | - Joana Pereira
- Graz University of Technology, Institute of Neural Engineering, BCI-Lab, Graz, Austria
| | | | | | - Gernot R Müller-Putz
- Graz University of Technology, Institute of Neural Engineering, BCI-Lab, Graz, Austria.
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Borhani S, Kilmarx J, Saffo D, Ng L, Abiri R, Zhao X. Optimizing Prediction Model for a Noninvasive Brain-Computer Interface Platform Using Channel Selection, Classification, and Regression. IEEE J Biomed Health Inform 2019; 23:2475-2482. [PMID: 30640636 DOI: 10.1109/jbhi.2019.2892379] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
A brain-computer interface (BCI) platform can be utilized by a user to control an external device without making any overt movements. An EEG-based computer cursor control task is commonly used as a testbed for BCI applications. While traditional computer cursor control schemes are based on sensorimotor rhythm, a new scheme has recently been developed using imagined body kinematics (IBK) to achieve natural cursor movement in a shorter time of training. This article attempts to explore optimal decoding algorithms for an IBK paradigm using EEG signals with application to neural cursor control. The study is based on an offline analysis of 32 healthy subjects' training data. Various machine learning techniques were implemented to predict the kinematics of the computer cursor using EEG signals during the training tasks. Our results showed that a linear regression least squares model yielded the highest goodness-of-fit scores in the cursor kinematics model (70% in horizontal prediction and 40% in vertical prediction using a Theil-Sen regressor). Additionally, the contribution of each EEG channel on the predictability of cursor kinematics was examined for horizontal and vertical directions, separately. A directional classifier was also proposed to classify horizontal versus vertical cursor kinematics using EEG signals. By incorporating features extracted from specific frequency bands, we achieved 80% classification accuracy in differentiating horizontal and vertical cursor movements. The findings of the current study could facilitate a pathway to designing an optimized online neural cursor control.
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Schaeffer MC, Aksenova T. Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review. Front Neurosci 2018; 12:540. [PMID: 30158847 PMCID: PMC6104172 DOI: 10.3389/fnins.2018.00540] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 07/17/2018] [Indexed: 11/13/2022] Open
Abstract
Brain-Computer Interfaces (BCIs) are systems that establish a direct communication pathway between the users' brain activity and external effectors. They offer the potential to improve the quality of life of motor-impaired patients. Motor BCIs aim to permit severely motor-impaired users to regain limb mobility by controlling orthoses or prostheses. In particular, motor BCI systems benefit patients if the decoded actions reflect the users' intentions with an accuracy that enables them to efficiently interact with their environment. One of the main challenges of BCI systems is to adapt the BCI's signal translation blocks to the user to reach a high decoding accuracy. This paper will review the literature of data-driven and user-specific transducer design and identification approaches and it focuses on internally-paced motor BCIs. In particular, continuous kinematic biomimetic and mental-task decoders are reviewed. Furthermore, static and dynamic decoding approaches, linear and non-linear decoding, offline and real-time identification algorithms are considered. The current progress and challenges related to the design of clinical-compatible motor BCI transducers are additionally discussed.
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Affiliation(s)
| | - Tetiana Aksenova
- CEA, LETI, CLINATEC, MINATEC Campus, Université Grenoble Alpes, Grenoble, France
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Korik A, Siddique N, Sosnik R, Coyle D. E3D hand movement velocity reconstruction using power spectral density of EEG signals and neural network. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2015:8103-6. [PMID: 26738174 DOI: 10.1109/embc.2015.7320274] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Three dimensional (3D) limb motion trajectory is predictable with a non-invasive brain-computer interface (BCI). To date, most non-invasive motion trajectory prediction BCIs use potential values of electroencephalographic (EEG) signals as the input to a multiple linear regression (mLR) based kinetic data estimator. We investigated the possible improvement in accuracy of 3D hand movement prediction (i.e., the correlation of registered and reconstructed hand velocities) by replacing raw EEG potentials with spectrum power values of specific EEG bands. We also investigated if a non-linear neural network based estimator outperformed the mLR approach. The spectrum power model provided significantly higher accuracy (R~0.60) compared to the similar EEG potentials based approach (R~0.45). Additionally, when replacing the mLR based kinetic data estimation module with a feed-forward neural network (NN) we found the NN based spectrum power model provided higher accuracy (R~0.70) compared to the similar mLR based approach (R~0.60).
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Lopes Dias C, Sburlea AI, Müller-Putz GR. Masked and unmasked error-related potentials during continuous control and feedback. J Neural Eng 2018; 15:036031. [PMID: 29557346 DOI: 10.1088/1741-2552/aab806] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The detection of error-related potentials (ErrPs) in tasks with discrete feedback is well established in the brain-computer interface (BCI) field. However, the decoding of ErrPs in tasks with continuous feedback is still in its early stages. OBJECTIVE We developed a task in which subjects have continuous control of a cursor's position by means of a joystick. The cursor's position was shown to the participants in two different modalities of continuous feedback: normal and jittered. The jittered feedback was created to mimic the instability that could exist if participants controlled the trajectory directly with brain signals. APPROACH This paper studies the electroencephalographic (EEG)-measurable signatures caused by a loss of control over the cursor's trajectory, causing a target miss. MAIN RESULTS In both feedback modalities, time-locked potentials revealed the typical frontal-central components of error-related potentials. Errors occurring during the jittered feedback (masked errors) were delayed in comparison to errors occurring during normal feedback (unmasked errors). Masked errors displayed lower peak amplitudes than unmasked errors. Time-locked classification analysis allowed a good distinction between correct and error classes (average Cohen-[Formula: see text], average TPR = 81.8% and average TNR = 96.4%). Time-locked classification analysis between masked error and unmasked error classes revealed results at chance level (average Cohen-[Formula: see text], average TPR = 60.9% and average TNR = 58.3%). Afterwards, we performed asynchronous detection of ErrPs, combining both masked and unmasked trials. The asynchronous detection of ErrPs in a simulated online scenario resulted in an average TNR of 84.0% and in an average TPR of 64.9%. SIGNIFICANCE The time-locked classification results suggest that the masked and unmasked errors were indistinguishable in terms of classification. The asynchronous classification results suggest that the feedback modality did not hinder the asynchronous detection of ErrPs.
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Affiliation(s)
- Catarina Lopes Dias
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
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15
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Upper limb movements can be decoded from the time-domain of low-frequency EEG. PLoS One 2017; 12:e0182578. [PMID: 28797109 PMCID: PMC5552335 DOI: 10.1371/journal.pone.0182578] [Citation(s) in RCA: 97] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Accepted: 07/20/2017] [Indexed: 11/29/2022] Open
Abstract
How neural correlates of movements are represented in the human brain is of ongoing interest and has been researched with invasive and non-invasive methods. In this study, we analyzed the encoding of single upper limb movements in the time-domain of low-frequency electroencephalography (EEG) signals. Fifteen healthy subjects executed and imagined six different sustained upper limb movements. We classified these six movements and a rest class and obtained significant average classification accuracies of 55% (movement vs movement) and 87% (movement vs rest) for executed movements, and 27% and 73%, respectively, for imagined movements. Furthermore, we analyzed the classifier patterns in the source space and located the brain areas conveying discriminative movement information. The classifier patterns indicate that mainly premotor areas, primary motor cortex, somatosensory cortex and posterior parietal cortex convey discriminative movement information. The decoding of single upper limb movements is specially interesting in the context of a more natural non-invasive control of e.g., a motor neuroprosthesis or a robotic arm in highly motor disabled persons.
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Úbeda A, Azorín JM, Chavarriaga R, R Millán JD. Classification of upper limb center-out reaching tasks by means of EEG-based continuous decoding techniques. J Neuroeng Rehabil 2017; 14:9. [PMID: 28143603 PMCID: PMC5286813 DOI: 10.1186/s12984-017-0219-0] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Accepted: 01/17/2017] [Indexed: 11/18/2022] Open
Abstract
Background One of the current challenges in brain-machine interfacing is to characterize and decode upper limb kinematics from brain signals, e.g. to control a prosthetic device. Recent research work states that it is possible to do so based on low frequency EEG components. However, the validity of these results is still a matter of discussion. In this paper, we assess the feasibility of decoding upper limb kinematics from EEG signals in center-out reaching tasks during passive and active movements. Methods The decoding of arm movement was performed using a multidimensional linear regression. Passive movements were analyzed using the same methodology to study the influence of proprioceptive sensory feedback in the decoding. Finally, we evaluated the possible advantages of classifying reaching targets, instead of continuous trajectories. Results The results showed that arm movement decoding was significantly above chance levels. The results also indicated that EEG slow cortical potentials carry significant information to decode active center-out movements. The classification of reached targets allowed obtaining the same conclusions with a very high accuracy. Additionally, the low decoding performance obtained from passive movements suggests that discriminant modulations of low-frequency neural activity are mainly related to the execution of movement while proprioceptive feedback is not sufficient to decode upper limb kinematics. Conclusions This paper contributes to the assessment of feasibility of using linear regression methods to decode upper limb kinematics from EEG signals. From our findings, it can be concluded that low frequency bands concentrate most of the information extracted from upper limb kinematics decoding and that decoding performance of active movements is above chance levels and mainly related to the activation of cortical motor areas. We also show that the classification of reached targets from decoding approaches may be a more suitable real-time methodology than a direct decoding of hand position.
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Affiliation(s)
- Andrés Úbeda
- Brain-Machine Interface Systems Lab, Miguel Hernández University, Av. de la Universidad, S/N, Elche, 03202, Spain
| | - José M Azorín
- Brain-Machine Interface Systems Lab, Miguel Hernández University, Av. de la Universidad, S/N, Elche, 03202, Spain
| | - Ricardo Chavarriaga
- Defitech Chair in Brain-Machine Interface (CNBI), École Polytechnique Fédérale de Lausanne (EPFL), Chemin des Mines 9, Geneva, CH-1202, Switzerland.
| | - José Del R Millán
- Defitech Chair in Brain-Machine Interface (CNBI), École Polytechnique Fédérale de Lausanne (EPFL), Chemin des Mines 9, Geneva, CH-1202, Switzerland
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17
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Huggins JE, Guger C, Ziat M, Zander TO, Taylor D, Tangermann M, Soria-Frisch A, Simeral J, Scherer R, Rupp R, Ruffini G, Robinson DKR, Ramsey NF, Nijholt A, Müller-Putz G, McFarland DJ, Mattia D, Lance BJ, Kindermans PJ, Iturrate I, Herff C, Gupta D, Do AH, Collinger JL, Chavarriaga R, Chase SM, Bleichner MG, Batista A, Anderson CW, Aarnoutse EJ. Workshops of the Sixth International Brain-Computer Interface Meeting: brain-computer interfaces past, present, and future. BRAIN-COMPUTER INTERFACES 2017; 4:3-36. [PMID: 29152523 PMCID: PMC5693371 DOI: 10.1080/2326263x.2016.1275488] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The Sixth International Brain-Computer Interface (BCI) Meeting was held 30 May-3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain-machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.
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Affiliation(s)
- Jane E. Huggins
- Department of Physical Medicine and Rehabilitation, Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Christoph Guger
- G.Tec Medical Engineering GmbH, Guger Technologies OG, Schiedlberg, Austria
| | - Mounia Ziat
- Psychology Department, Northern Michigan University, Marquette, MI, USA
| | - Thorsten O. Zander
- Team PhyPA, Biological Psychology and Neuroergonomics, Technical University of Berlin, Berlin, Germany
| | | | - Michael Tangermann
- Cluster of Excellence BrainLinks-BrainTools, University of Freiburg, Germany
| | | | - John Simeral
- Ctr. For Neurorestoration and Neurotechnology, Rehab. R&D Service, Dept. of VA Medical Center, School of Engineering, Brown University, Providence, RI, USA
| | - Reinhold Scherer
- Institute of Neural Engineering, BCI- Lab, Graz University of Technology, Graz, Austria
| | - Rüdiger Rupp
- Section Experimental Neurorehabilitation, Spinal Cord Injury Center, University Hospital in Heidelberg, Heidelberg, Germany
| | - Giulio Ruffini
- Neuroscience Business Unit, Starlab Barcelona SLU, Barcelona, Spain
- Neuroelectrics Inc., Boston, USA
| | - Douglas K. R. Robinson
- Institute: Laboratoire Interdisciplinaire Sciences Innovations Sociétés (LISIS), Université Paris-Est Marne-la-Vallée, MARNE-LA-VALLÉE, France
| | - Nick F. Ramsey
- Dept Neurology & Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, University of Utrecht, Utrecht, Netherlands
| | - Anton Nijholt
- Faculty EEMCS, Enschede, University of Twente, The Netherlands & Imagineering Institute, Iskandar, Malaysia
| | - Gernot Müller-Putz
- Institute of Neural Engineering, BCI- Lab, Graz University of Technology, Graz, Austria
| | - Dennis J. McFarland
- New York State Department of Health, National Center for Adaptive Neurotechnologies, Wadsworth Center, Albany, New York USA
| | - Donatella Mattia
- Clinical Neurophysiology, Fondazione Santa Lucia, Neuroelectrical Imaging and BCI Lab, IRCCS, Rome, Italy
| | - Brent J. Lance
- Human Research and Engineering Directorate, U.S. Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD USA
| | | | - Iñaki Iturrate
- Defitech Chair in Brain–machine Interface (CNBI), Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, EPFL-STI-CNBI, Campus Biotech H4, Geneva, Switzerland
| | - Christian Herff
- Cognitive Systems Lab, University of Bremen, Bremen, Germany
| | - Disha Gupta
- Brain Mind Research Inst, Weill Cornell Medical College, Early Brain Injury and Recovery Lab, Burke Medical Research Inst, White Plains, New York, USA
| | - An H. Do
- Department of Neurology, UC Irvine Brain Computer Interface Lab, University of California, Irvine, CA, USA
| | - Jennifer L. Collinger
- Department of Physical Medicine and Rehabilitation, Department of Veterans Affairs, VA Pittsburgh Healthcare System, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ricardo Chavarriaga
- Defitech Chair in Brain–machine Interface (CNBI), Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, EPFL-STI-CNBI, Campus Biotech H4, Geneva, Switzerland
| | - Steven M. Chase
- Center for the Neural Basis of Cognition and Department Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Martin G. Bleichner
- Neuropsychology Lab, Department of Psychology, European Medical School, Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany
| | - Aaron Batista
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA USA
| | - Charles W. Anderson
- Department of Computer Science, Colorado State University, Fort Collins, CO USA
| | - Erik J. Aarnoutse
- Brain Center Rudolf Magnus, Dept Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
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18
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EEG neural correlates of goal-directed movement intention. Neuroimage 2017; 149:129-140. [PMID: 28131888 PMCID: PMC5387183 DOI: 10.1016/j.neuroimage.2017.01.030] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Revised: 01/11/2017] [Accepted: 01/13/2017] [Indexed: 11/21/2022] Open
Abstract
Using low-frequency time-domain electroencephalographic (EEG) signals we show, for the same type of upper limb movement, that goal-directed movements have different neural correlates than movements without a particular goal. In a reach-and-touch task, we explored the differences in the movement-related cortical potentials (MRCPs) between goal-directed and non-goal-directed movements. We evaluated if the detection of movement intention was influenced by the goal-directedness of the movement. In a single-trial classification procedure we found that classification accuracies are enhanced if there is a goal-directed movement in mind. Furthermore, by using the classifier patterns and estimating the corresponding brain sources, we show the importance of motor areas and the additional involvement of the posterior parietal lobule in the discrimination between goal-directed movements and non-goal-directed movements. We discuss next the potential contribution of our results on goal-directed movements to a more reliable brain-computer interface (BCI) control that facilitates recovery in spinal-cord injured or stroke end-users.
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19
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Müller-Putz GR, Plank P, Stadlbauer B, Statthaler K, Uroko JB. 15 Years of Evolution of Non-Invasive EEG-Based Methods for Restoring Hand & Arm Function with Motor Neuroprosthetics in Individuals with High Spinal Cord Injury: A Review of Graz BCI Research. ACTA ACUST UNITED AC 2017. [DOI: 10.4236/jbise.2017.106024] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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20
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Batres-Mendoza P, Montoro-Sanjose CR, Guerra-Hernandez EI, Almanza-Ojeda DL, Rostro-Gonzalez H, Romero-Troncoso RJ, Ibarra-Manzano MA. Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals. SENSORS 2016; 16:s16030336. [PMID: 26959029 PMCID: PMC4813911 DOI: 10.3390/s16030336] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Revised: 02/26/2016] [Accepted: 02/29/2016] [Indexed: 11/16/2022]
Abstract
Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signal analysis technique can extract features to represent brain activity related to motor imagery accurately in various mental states. Experimental tests in which users where shown visual graphical cues related to left and right movements were used to collect BCI-recorded signals. These signals were then classified using decision trees (DT), support vector machine (SVM) and k-nearest neighbor (KNN) techniques. The quantitative analysis of the classifiers demonstrates that this technique can be used as an alternative in the EEG-signal modeling phase to identify mental states.
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Affiliation(s)
- Patricia Batres-Mendoza
- Laboratorio de Sistemas Bioinspirados, Departamento de Ingeniería Electrónica, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico.
| | - Carlos R Montoro-Sanjose
- Departamento de Arte y Empresa, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico.
- Cuerpo Académico de Telemática, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico.
| | - Erick I Guerra-Hernandez
- Laboratorio de Sistemas Bioinspirados, Departamento de Ingeniería Electrónica, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico.
| | - Dora L Almanza-Ojeda
- Laboratorio de Procesamiento Digital de Señales, Departamento de Ingeniería Electrónica, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico.
| | - Horacio Rostro-Gonzalez
- Laboratorio de Sistemas Bioinspirados, Departamento de Ingeniería Electrónica, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico.
- Cuerpo Académico de Telemática, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico.
| | - Rene J Romero-Troncoso
- Cuerpo Académico de Telemática, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico.
- Departamento de Ingeniería Electrónica, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico.
| | - Mario A Ibarra-Manzano
- Cuerpo Académico de Telemática, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico.
- Laboratorio de Procesamiento Digital de Señales, Departamento de Ingeniería Electrónica, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico.
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21
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Ofner P, Muller-Putz GR. Movement target decoding from EEG and the corresponding discriminative sources: A preliminary study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:1468-71. [PMID: 26736547 DOI: 10.1109/embc.2015.7318647] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Brain-computer interfaces (BCIs) can detect movement imaginations (MI) which can act as a control signal for a neuroprosthesis of a paralyzed person. However, today's non-invasive BCIs can only detect simply qualities of MI, like what body part is subjected to MI. More advanced future non-invasive BCIs should be able to detect many qualities of MI to allow a natural control of a neuroprosthesis. In this preliminary study, we decoded movement targets during a self-paced center-out reaching task, and calculated corresponding spatial patterns in the source space. We were able to decode the movement targets with significant classification accuracy from one out of three subjects during the movement planning phase. This subject showed a distinct spatial pattern over the central motor area.
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22
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Assessing movement factors in upper limb kinematics decoding from EEG signals. PLoS One 2015; 10:e0128456. [PMID: 26020525 PMCID: PMC4447410 DOI: 10.1371/journal.pone.0128456] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2014] [Accepted: 04/27/2015] [Indexed: 11/29/2022] Open
Abstract
The past decades have seen the rapid development of upper limb kinematics decoding techniques by performing intracortical recordings of brain signals. However, the use of non-invasive approaches to perform similar decoding procedures is still in its early stages. Recent studies show that there is a correlation between electroencephalographic (EEG) signals and hand-reaching kinematic parameters. From these studies, it could be concluded that the accuracy of upper limb kinematics decoding depends, at least partially, on the characteristics of the performed movement. In this paper, we have studied upper limb movements with different speeds and trajectories in a controlled environment to analyze the influence of movement variability in the decoding performance. To that end, low frequency components of the EEG signals have been decoded with linear models to obtain the position of the volunteer’s hand during performed trajectories grasping the end effector of a planar manipulandum. The results confirm that it is possible to obtain kinematic information from low frequency EEG signals and show that decoding performance is significantly influenced by movement variability and tracking accuracy as continuous and slower movements improve the accuracy of the decoder. This is a key factor that should be taken into account in future experimental designs.
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23
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Ofner P, Müller-Putz GR. Using a noninvasive decoding method to classify rhythmic movement imaginations of the arm in two planes. IEEE Trans Biomed Eng 2014; 62:972-81. [PMID: 25494495 DOI: 10.1109/tbme.2014.2377023] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
A brain-computer interface (BCI) can help to overcome movement deficits in persons with spinal-cord injury. Ideally, such a BCI detects detailed movement imaginations, i.e., trajectories, and transforms them into a control signal for a neuroprosthesis or a robotic arm restoring movement. Robotic arms have already been controlled successfully by means of invasive recording techniques, and executed movements have been reconstructed using noninvasive decoding techniques. However, it is unclear if detailed imagined movements can be decoded noninvasively using electroencephalography (EEG). We made progress toward imagined movement decoding and successfully classified horizontal and vertical imagined rhythmic movements of the right arm in healthy subjects using EEG. Notably, we used an experimental design which avoided muscle and eye movements to prevent classification results being affected. To classify imagined movements of the same limb, we decoded the movement trajectories and correlated them with assumed movement trajectories (horizontal and vertical). We then assigned the decoded movements to the assumed movements with the higher correlation. To train the decoder, we applied partial least squares, which allowed us to interpret the classifier weights although channels were highly correlated. To conclude, we showed the classification of imagined movements of one limb in two different movement planes in seven out of nine subjects. Furthermore, we found a strong involvement of the supplementary motor area. Finally, as our classifier was based on the decoding approach, we indirectly showed the decoding of imagined movements.
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24
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Kim JH, Bießmann F, Lee SW. Decoding Three-Dimensional Trajectory of Executed and Imagined Arm Movements From Electroencephalogram Signals. IEEE Trans Neural Syst Rehabil Eng 2014; 23:867-76. [PMID: 25474811 DOI: 10.1109/tnsre.2014.2375879] [Citation(s) in RCA: 89] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Decoding motor commands from noninvasively measured neural signals has become important in brain-computer interface (BCI) research. Applications of BCI include neurorehabilitation after stroke and control of limb prostheses. Until now, most studies have tested simple movement trajectories in two dimensions by using constant velocity profiles. However, most real-world scenarios require much more complex movement trajectories and velocity profiles. In this study, we decoded motor commands in three dimensions from electroencephalography (EEG) recordings while the subjects either executed or observed/imagined complex upper limb movement trajectories. We compared the accuracy of simple linear methods and nonlinear methods. In line with previous studies our results showed that linear decoders are an efficient and robust method for decoding motor commands. However, while we took the same precautions as previous studies to suppress eye-movement related EEG contamination, we found that subtracting residual electro-oculogram activity from the EEG data resulted in substantially lower motor decoding accuracy for linear decoders. This effect severely limits the transfer of previous results to practical applications in which neural activation is targeted. We observed that nonlinear methods showed no such drop in decoding performance. Our results demonstrate that eye-movement related contamination of brain signals constitutes a severe problem for decoding motor signals from EEG data. These results are important for developing accurate decoders of motor signal from neural signals for use with BCI-based neural prostheses and neurorehabilitation in real-world scenarios.
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25
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26
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Ahn M, Ahn S, Hong JH, Cho H, Kim K, Kim BS, Chang JW, Jun SC. Gamma band activity associated with BCI performance: simultaneous MEG/EEG study. Front Hum Neurosci 2013; 7:848. [PMID: 24367322 PMCID: PMC3853408 DOI: 10.3389/fnhum.2013.00848] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2013] [Accepted: 11/21/2013] [Indexed: 11/16/2022] Open
Abstract
While brain computer interface (BCI) can be employed with patients and healthy subjects, there are problems that must be resolved before BCI can be useful to the public. In the most popular motor imagery (MI) BCI system, a significant number of target users (called “BCI-Illiterates”) cannot modulate their neuronal signals sufficiently to use the BCI system. This causes performance variability among subjects and even among sessions within a subject. The mechanism of such BCI-Illiteracy and possible solutions still remain to be determined. Gamma oscillation is known to be involved in various fundamental brain functions, and may play a role in MI. In this study, we investigated the association of gamma activity with MI performance among subjects. Ten simultaneous MEG/EEG experiments were conducted; MI performance for each was estimated by EEG data, and the gamma activity associated with BCI performance was investigated with MEG data. Our results showed that gamma activity had a high positive correlation with MI performance in the prefrontal area. This trend was also found across sessions within one subject. In conclusion, gamma rhythms generated in the prefrontal area appear to play a critical role in BCI performance.
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Affiliation(s)
- Minkyu Ahn
- School of Information and Communications, Gwangju Institute of Science and Technology Gwangju, South Korea
| | - Sangtae Ahn
- School of Information and Communications, Gwangju Institute of Science and Technology Gwangju, South Korea
| | - Jun H Hong
- School of Information and Communications, Gwangju Institute of Science and Technology Gwangju, South Korea
| | - Hohyun Cho
- School of Information and Communications, Gwangju Institute of Science and Technology Gwangju, South Korea
| | - Kiwoong Kim
- Korea Research Institute of Standards and Science Daejeon, South Korea
| | - Bong S Kim
- Department of Neurosurgery, Brain Research Institute, Yonsei University College of Medicine Seoul, South Korea
| | - Jin W Chang
- Department of Neurosurgery, Brain Research Institute, Yonsei University College of Medicine Seoul, South Korea
| | - Sung C Jun
- School of Information and Communications, Gwangju Institute of Science and Technology Gwangju, South Korea ; Wadsworth Center, New York State Health Department, Albany NY, USA
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27
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Antelis JM, Montesano L, Ramos-Murguialday A, Birbaumer N, Minguez J. On the usage of linear regression models to reconstruct limb kinematics from low frequency EEG signals. PLoS One 2013; 8:e61976. [PMID: 23613992 PMCID: PMC3629197 DOI: 10.1371/journal.pone.0061976] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2012] [Accepted: 03/18/2013] [Indexed: 12/03/2022] Open
Abstract
Several works have reported on the reconstruction of 2D/3D limb kinematics from low-frequency EEG signals using linear regression models based on positive correlation values between the recorded and the reconstructed trajectories. This paper describes the mathematical properties of the linear model and the correlation evaluation metric that may lead to a misinterpretation of the results of this type of decoders. Firstly, the use of a linear regression model to adjust the two temporal signals (EEG and velocity profiles) implies that the relevant component of the signal used for decoding (EEG) has to be in the same frequency range as the signal to be decoded (velocity profiles). Secondly, the use of a correlation to evaluate the fitting of two trajectories could lead to overly-optimistic results as this metric is invariant to scale. Also, the correlation has a non-linear nature that leads to higher values for sinus/cosinus-like signals at low frequencies. Analysis of these properties on the reconstruction results was carried out through an experiment performed in line with previous studies, where healthy participants executed predefined reaching movements of the hand in 3D space. While the correlations of limb velocity profiles reconstructed from low-frequency EEG were comparable to studies in this domain, a systematic statistical analysis revealed that these results were not above the chance level. The empirical chance level was estimated using random assignments of recorded velocity profiles and EEG signals, as well as combinations of randomly generated synthetic EEG with recorded velocity profiles and recorded EEG with randomly generated synthetic velocity profiles. The analysis shows that the positive correlation results in this experiment cannot be used as an indicator of successful trajectory reconstruction based on a neural correlate. Several directions are herein discussed to address the misinterpretation of results as well as the implications on previous invasive and non-invasive works.
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Affiliation(s)
- Javier M Antelis
- Aragon Institute of Engineering Research, I3A, University of Zaragoza, Zaragoza, Aragon, Spain.
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