RT Journal Article
T1 Is EMG a Viable Alternative to BCI for Detecting Movement Intention in Severe Stroke?
A1 Balasubramanian, Sivakumar
A1 Garcia-Cossio, Eliana
A1 Birbaumer, Niels
A1 Burdet, Etienne
A1 Ramos-Murguialday, Ander
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.
SN 0018-9294
YR 2018
FD 2018-12
LK https://hdl.handle.net/11556/4574
UL https://hdl.handle.net/11556/4574
LA eng
NO 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
NO Publisher Copyright: © 2018 IEEE.
NO Manuscript received October 4, 2017; revised January 22, 2018; accepted March 4, 2018. Date of publication March 21, 2018; date of current version November 20, 2018. This work was supported in part by the European Commission Under Grant EU-FP7 HUMOUR (ICT 231554), Grant CONTEST (ITN 317488), Grant EU-H2020 COGI-MON (ICT 644727), and Grant COST ACTION TD1006 European Network on Robotics for NeuroRehabilitation, in part by the Deutsche Forschungsgemeinschaft (DFG), in part by the Baden-Württemberg Stiftung (GRUENS, ROB-1), in part by the Natural Science Fun-dation of China under Grant NSFC 31450110072, in part by the Bundes Ministerium für Bildung und Forschung BMBF under Grant MO-TORBIC (FKZ 13GW0053) and Grant AMORSA (16SV7754), and in part by the UKIERI under Grant UKUTP201100135. (Etienne Burdet and An-der Ramos-Murguialday are senior authors.) (Corresponding author: Ander Ramos-Murguialday.) S. Balasubramanian is with the Department of Bioengineering, Christian Medical College. This work was supported in part by the European Commission Under Grant EU-FP7 HUMOUR (ICT 231554), Grant CONTEST (ITN 317488), Grant EU-H2020 COGIMON (ICT 644727), and Grant COST ACTION TD1006 European Network on Robotics for NeuroRehabilitation, in part by the Deutsche Forschungsgemeinschaft (DFG), in part by the Baden-Württemberg Stiftung (GRUENS, ROB-1), in part by the Natural Science Fundation of China under Grant NSFC 31450110072, in part by the Bundes Ministerium für Bildung und Forschung BMBF under Grant MOTORBIC (FKZ 13GW0053) and Grant AMORSA (16SV7754), and in part by the UKIERI under Grant UKUTP201100135. (Etienne Burdet and Ander Ramos-Murguialday are senior authors.) (Corresponding author: Ander Ramos-Murguialday.)
DS TECNALIA Publications
RD 26 jul 2024