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Decoding Finger Tapping With the Affected Hand in Chronic Stroke Patients During Motor Imagery and Execution

Authors
Lee, MinjiJeong, Ji-HoonKim, Yun-HeeLee, Seong-Whan
Issue Date
2021
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Deep learning; Electroencephalography; Performance evaluation; Presses; Stroke; Stroke (medical condition); Task analysis; Thumb; brain-computer interface; finger tapping classification; motor imagery
Citation
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, v.29, pp.1099 - 1109
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
Volume
29
Start Page
1099
End Page
1109
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/138492
DOI
10.1109/TNSRE.2021.3087506
ISSN
1534-4320
Abstract
In stroke rehabilitation, motor imagery based on a brain-computer interface is an extremely useful method to control an external device and utilize neurofeedback. Many studies have reported on the classification performance of motor imagery to decode individual fingers in stroke patients compared with healthy controls. However, classification performance for a given limb is still low because the differences between patients owing to brain reorganization after stroke are not considered. We used electroencephalography signals from eleven healthy controls and eleven stroke patients in this study. The subjects performed a finger tapping task during motor execution, and motor imagery was performed with the dominant and affected hands in the healthy controls and stroke patients, respectively. All fingers except for the thumb were classified using the proposed framework based on a voting module. The averaged four-class accuracies during motor execution and motor imagery were 53.16 +/- 8.42% and 46.94 +/- 5.99% for the healthy controls and 53.17 +/- 14.09% and 66.00 +/- 14.96% for the stroke patients, respectively. Importantly, the classification accuracies in the stroke patients were statistically higher than those in healthy controls during motor imagery. However, there was no significant difference between the accuracies of motor execution and motor imagery. These findings show the potential for high classification performance for a given limb during motor imagery in stroke patients. These results can also provide insights into controlling an external device on the basis of a brain-computer interface.
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