Imperial College London
Alternate

ProfessorEtienneBurdet

Faculty of Engineering, Department of Bioengineering

Professor of Human Robotics
 
 
 
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Contact

 

e.burdet Website

 
 
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Location

 

419BSir Michael Uren HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Balasubramanian:2018:10.1109/TBME.2018.2817688,
author = {Balasubramanian, S and Garcia-Cossio, E and Birbaumer, N and Burdet, E and Ramos-Murguialday, A},
doi = {10.1109/TBME.2018.2817688},
journal = {IEEE Transactions on Biomedical Engineering},
pages = {2790--2797},
title = {Is EMG a viable alternative to BCI for detecting movement intention in severe stroke?},
url = {http://dx.doi.org/10.1109/TBME.2018.2817688},
volume = {65},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Balasubramanian,S
AU  - Garcia-Cossio,E
AU  - Birbaumer,N
AU  - Burdet,E
AU  - Ramos-Murguialday,A
DO  - 10.1109/TBME.2018.2817688
EP  - 2797
PY  - 2018///
SN  - 0018-9294
SP  - 2790
TI  - Is EMG a viable alternative to BCI for detecting movement intention in severe stroke?
T2  - IEEE Transactions on Biomedical Engineering
UR  - http://dx.doi.org/10.1109/TBME.2018.2817688
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000451253600014&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/70585
VL  - 65
ER  -
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