Is EMG a Viable Alternative to BCI for Detecting Movement Intention in Severe Stroke?

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URI: https://hdl.handle.net/11556/4574
ISSN: 0018-9294
DOI: 10.1109/TBME.2018.2817688
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2018-12
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Abstract
Objective: In light of the shortcomings of current restorative brain-computer interfaces (BCI), this study investigated the possibility of using EMG to detect hand/wrist extension movement intention to trigger robot-assisted training in individuals without residual movements. Methods: We compared movement intention detection using an EMG detector with a sensorimotor rhythm based EEG-BCI using only ipsilesional activity. This was carried out on data of 30 severely affected chronic stroke patients from a randomized control trial using an EEG-BCI for robot-assisted training. Results: The results indicate the feasibility of using EMG to detect movement intention in this severely handicapped population; probability of detecting EMG when patients attempted to move was higher (p < 0.001) than at rest. Interestingly, 22 out of 30 (or 73%) patients had sufficiently strong EMG in their finger/wrist extensors. Furthermore, in patients with detectable EMG, there was poor agreement between the EEG and EMG intent detectors, which indicates that these modalities may detect different processes. Conclusion : A substantial segment of severely affected stroke patients may benefit from EMG-based assisted therapy. When compared to EEG, a surface EMG interface requires less preparation time, which is easier to don/doff, and is more compact in size. Significance: This study shows that a large proportion of severely affected stroke patients have residual EMG, which yields a direct and practical way to trigger robot-assisted training.
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Publisher Copyright: © 2018 IEEE.
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BCI , EMG , Stroke , movement intention , neurorehabilitation , Biomedical Engineering , SDG 3 - Good Health and Well-being
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journal article
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Balasubramanian , S , Garcia-Cossio , E , Birbaumer , N , Burdet , E & Ramos-Murguialday , A 2018 , ' Is EMG a Viable Alternative to BCI for Detecting Movement Intention in Severe Stroke? ' , IEEE Transactions on Biomedical Engineering , vol. 65 , no. 12 , 8320831 , pp. 2790-2797 . https://doi.org/10.1109/TBME.2018.2817688