A large clinical study on the ability of stroke patients to use an EEG-based motor imagery brain-computer interface.
Brain-computer interface (BCI) technology has the prospects of helping stroke survivors by enabling the interaction with their environ ment through brain signals rather than through muscles, and restoring motor function by inducing activity-dependent brain plasticity. This paper presents a clinical study on the extent of detectable brain signals from a large population of stroke patients in using EEG-based motor imagery BCI. EEG data were collected from 54 stroke patients whereby finger tapping and motor imagery of the stroke-affected hand were performed by 8 and 46 patients, respectively. EEG data from 11 patients who gave further consent to perform motor imagery were also collected for second calibration and third independent test sessions conducted on separate days. Off-line accuracies of classifying the two classes of EEG from finger tapping or motor imagery of the stroke-affected hand versus the EEG from background rest were then assessed and compared to 16 healthy subjects. The mean off-line accuracy of detecting motor imagery by the 46 patients (mu=0.74) was significantly lower than finger tapping by 8 patients (mu=0.87, p=0.008), but not significantly lower than motor imagery by healthy subjects (mu=0.78, p=0.23). Six stroke patients performed motor imagery at chance level, and no correlation was found between the accuracies of detecting motor imagery and their motor impairment in terms of Fugl-Meyer Assessment (p=0.29). The off-line accuracies of the 11 patients in the second session (mu=0.76) were not significantly different from the first session (mu=0.72, p=0.16), or from the on-line accuracies of the third independent test session (mu=0.82, p=0.14). Hence this study showed that the majority of stroke patients could use EEG-based motor imagery BCI.
Institute for Infocomm Research Agency for Science Technology and Research, Singapore. kkang@i2r.a-star.edu.sg
Kai Keng
KK
Ang
Cuntai
C
Guan
Karen Sui Geok
KS
Chua
Beng Ti
BT
Ang
Christopher Wee Keong
CW
Kuah
Chuanchu
C
Wang
Kok Soon
KS
Phua
Zheng Yang
ZY
Chin
Haihong
H
Zhang
D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D016022:Case-Control Studies; D004569:Electroencephalography / Q000379:methods*; D056228:Feedback, Sensory / Q000502:physiology; D005260:Female; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008297:Male; D008328:Man-Machine Systems; D008875:Middle Aged; D009043:Motor Activity / Q000502:physiology; D020521:Stroke / Q000503:physiopathology; D000071939:Stroke Rehabilitation; D013647:Task Performance and Analysis; D014584:User-Computer Interface