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Pereira J, Kobler R, Ofner P, Schwarz A, Müller-Putz GR. Online detection of movement during natural and self-initiated reach-and-grasp actions from EEG signals. J Neural Eng 2021; 18. [PMID: 34130267 DOI: 10.1088/1741-2552/ac0b52] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 06/15/2021] [Indexed: 11/11/2022]
Abstract
Movement intention detection using electroencephalography (EEG) is a challenging but essential component of brain-computer interfaces (BCIs) for people with motor disabilities.Objective.The goal of this study is to develop a new experimental paradigm to perform asynchronous online detection of movement based on low-frequency time-domain EEG features, concretely on movement-related cortical potentials. The paradigm must be easily transferable to people without any residual upper-limb movement function and the BCI must be independent of upper-limb movement onset measurements and external cues.Approach. In a study with non-disabled participants, we evaluated a novel BCI paradigm to detect self-initiated reach-and-grasp movements. Two experimental conditions were involved. In one condition, participants performed reach-and-grasp movements to a target and simultaneously shifted their gaze towards it. In a control condition, participants solely shifted their gaze towards the target (oculomotor task). The participants freely decided when to initiate the tasks. After eye artefact correction, the EEG signals were time-locked to the saccade onset and the resulting amplitude features were exploited on a hierarchical classification approach to detect movement asynchronously.Main results. With regards to BCI performance, 54.1% (14.4% SD) of the movements were correctly identified, and all participants achieved a performance above chance-level (around 12%). An average of 21.5% (14.1% SD) of the oculomotor tasks were falsely detected as upper-limb movement. In an additional rest condition, 1.7 (1.6 SD) false positives per minute were measured. Through source imaging, movement information was mapped to sensorimotor, posterior parietal and occipital areas.Significance. We present a novel approach for movement detection using EEG signals which does not rely on upper-limb movement onset measurements or on the presentation of external cues. The participants' behaviour closely matches the natural behaviour during goal-directed reach-and-grasp movements, which also constitutes an advantage with respect to current BCI protocols.
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Affiliation(s)
- Joana Pereira
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Reinmar Kobler
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Patrick Ofner
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Andreas Schwarz
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
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Bockbrader MA, Francisco G, Lee R, Olson J, Solinsky R, Boninger ML. Brain Computer Interfaces in Rehabilitation Medicine. PM R 2019; 10:S233-S243. [PMID: 30269808 DOI: 10.1016/j.pmrj.2018.05.028] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 05/22/2018] [Accepted: 05/31/2018] [Indexed: 12/24/2022]
Abstract
One innovation currently influencing physical medicine and rehabilitation is brain-computer interface (BCI) technology. BCI systems used for motor control record neural activity associated with thoughts, perceptions, and motor intent; decode brain signals into commands for output devices; and perform the user's intended action through an output device. BCI systems used for sensory augmentation transduce environmental stimuli into neural signals interpretable by the central nervous system. Both types of systems have potential for reducing disability by facilitating a user's interaction with the environment. Investigational BCI systems are being used in the rehabilitation setting both as neuroprostheses to replace lost function and as potential plasticity-enhancing therapy tools aimed at accelerating neurorecovery. Populations benefitting from motor and somatosensory BCI systems include those with spinal cord injury, motor neuron disease, limb amputation, and stroke. This article discusses the basic components of BCI for rehabilitation, including recording systems and locations, signal processing and translation algorithms, and external devices controlled through BCI commands. An overview of applications in motor and sensory restoration is provided, along with ethical questions and user perspectives regarding BCI technology.
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Affiliation(s)
- Marcia A Bockbrader
- Department of Physical Medicine & Rehabilitation, The Ohio State University, 480 Medical Center Dr, Columbus, OH 43210; and Neurological Institute, Ohio State University Wexner Medical Center, Columbus, OH(∗).
| | - Gerard Francisco
- Department of Physical Medicine & Rehabilitation, The University of Texas, Houston, TX(†)
| | - Ray Lee
- Department of Orthopaedic and Rehabilitation, Schwab Rehabilitation Hospital, University of Chicago, Chicago, IL(‡)
| | - Jared Olson
- Department of Physical Medicine and Rehabilitation, University of Colorado, Aurora, CO(§)
| | - Ryan Solinsky
- Spaulding Rehabilitation Hospital, Boston; and Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA(¶)
| | - Michael L Boninger
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh; and VA Pittsburgh Health Care System, Pittsburgh, PA(#)
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Bockbrader M. Upper limb sensorimotor restoration through brain–computer interface technology in tetraparesis. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2019. [DOI: 10.1016/j.cobme.2019.09.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Gant K, Guerra S, Zimmerman L, Parks BA, Prins NW, Prasad A. EEG-controlled functional electrical stimulation for hand opening and closing in chronic complete cervical spinal cord injury. Biomed Phys Eng Express 2018. [DOI: 10.1088/2057-1976/aabb13] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Pereira J, Sburlea AI, Müller-Putz GR. EEG patterns of self-paced movement imaginations towards externally-cued and internally-selected targets. Sci Rep 2018; 8:13394. [PMID: 30190543 PMCID: PMC6127278 DOI: 10.1038/s41598-018-31673-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 08/23/2018] [Indexed: 11/25/2022] Open
Abstract
In this study, we investigate the neurophysiological signature of the interacting processes which lead to a single reach-and-grasp movement imagination (MI). While performing this task, the human healthy participants could either define their movement targets according to an external cue, or through an internal selection process. After defining their target, they could start the MI whenever they wanted. We recorded high density electroencephalographic (EEG) activity and investigated two neural correlates: the event-related potentials (ERPs) associated with the target selection, which reflect the perceptual and cognitive processes prior to the MI, and the movement-related cortical potentials (MRCPs), associated with the planning of the self-paced MI. We found differences in frontal and parietal areas between the late ERP components related to the internally-driven selection and the externally-cued process. Furthermore, we could reliably estimate the MI onset of the self-paced task. Next, we extracted MRCP features around the MI onset to train classifiers of movement vs. rest directly on self-paced MI data. We attained performance significantly higher than chance level for both time-locked and asynchronous classification. These findings contribute to the development of more intuitive brain-computer interfaces in which movement targets are defined internally and the movements are self-paced.
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Affiliation(s)
- Joana Pereira
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
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Jochumsen M, Niazi IK, Nedergaard RW, Navid MS, Dremstrup K. Effect of subject training on a movement-related cortical potential-based brain-computer interface. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.11.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Grimm F, Walter A, Spüler M, Naros G, Rosenstiel W, Gharabaghi A. Hybrid Neuroprosthesis for the Upper Limb: Combining Brain-Controlled Neuromuscular Stimulation with a Multi-Joint Arm Exoskeleton. Front Neurosci 2016; 10:367. [PMID: 27555805 PMCID: PMC4977295 DOI: 10.3389/fnins.2016.00367] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Accepted: 07/25/2016] [Indexed: 11/13/2022] Open
Abstract
Brain-machine interface-controlled (BMI) neurofeedback training aims to modulate cortical physiology and is applied during neurorehabilitation to increase the responsiveness of the brain to subsequent physiotherapy. In a parallel line of research, robotic exoskeletons are used in goal-oriented rehabilitation exercises for patients with severe motor impairment to extend their range of motion (ROM) and the intensity of training. Furthermore, neuromuscular electrical stimulation (NMES) is applied in neurologically impaired patients to restore muscle strength by closing the sensorimotor loop. In this proof-of-principle study, we explored an integrated approach for providing assistance as needed to amplify the task-related ROM and the movement-related brain modulation during rehabilitation exercises of severely impaired patients. For this purpose, we combined these three approaches (BMI, NMES, and exoskeleton) in an integrated neuroprosthesis and studied the feasibility of this device in seven severely affected chronic stroke patients who performed wrist flexion and extension exercises while receiving feedback via a virtual environment. They were assisted by a gravity-compensating, seven degree-of-freedom exoskeleton which was attached to the paretic arm. NMES was applied to the wrist extensor and flexor muscles during the exercises and was controlled by a hybrid BMI based on both sensorimotor cortical desynchronization (ERD) and electromyography (EMG) activity. The stimulation intensity was individualized for each targeted muscle and remained subthreshold, i.e., induced no overt support. The hybrid BMI controlled the stimulation significantly better than the offline analyzed ERD (p = 0.028) or EMG (p = 0.021) modality alone. Neuromuscular stimulation could be well integrated into the exoskeleton-based training and amplified both the task-related ROM (p = 0.009) and the movement-related brain modulation (p = 0.019). Combining a hybrid BMI with neuromuscular stimulation and antigravity assistance augments upper limb function and brain activity during rehabilitation exercises and may thus provide a novel restorative framework for severely affected stroke patients.
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Affiliation(s)
- Florian Grimm
- Division of Functional and Restorative Neurosurgery, Centre for Integrative Neuroscience, Eberhard Karls University Tuebingen Tuebingen, Germany
| | - Armin Walter
- Department of Computer Engineering, Wilhelm Schickard Institute for Computer Science, Eberhard Karls University Tuebingen Tuebingen, Germany
| | - Martin Spüler
- Department of Computer Engineering, Wilhelm Schickard Institute for Computer Science, Eberhard Karls University Tuebingen Tuebingen, Germany
| | - Georgios Naros
- Division of Functional and Restorative Neurosurgery, Centre for Integrative Neuroscience, Eberhard Karls University Tuebingen Tuebingen, Germany
| | - Wolfgang Rosenstiel
- Department of Computer Engineering, Wilhelm Schickard Institute for Computer Science, Eberhard Karls University Tuebingen Tuebingen, Germany
| | - Alireza Gharabaghi
- Division of Functional and Restorative Neurosurgery, Centre for Integrative Neuroscience, Eberhard Karls University Tuebingen Tuebingen, Germany
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Kreilinger A, Hiebel H, Muller-Putz GR. Single Versus Multiple Events Error Potential Detection in a BCI-Controlled Car Game With Continuous and Discrete Feedback. IEEE Trans Biomed Eng 2016; 63:519-29. [DOI: 10.1109/tbme.2015.2465866] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Halder S, Pinegger A, Käthner I, Wriessnegger SC, Faller J, Pires Antunes JB, Müller-Putz GR, Kübler A. Brain-controlled applications using dynamic P300 speller matrices. Artif Intell Med 2015; 63:7-17. [PMID: 25533310 DOI: 10.1016/j.artmed.2014.12.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Revised: 11/19/2014] [Accepted: 12/02/2014] [Indexed: 10/24/2022]
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