NDLI: A clinical study of motor imagery BCI performance in stroke by including calibration data from passive movement

Content Provider	IEEE Xplore Digital Library
Author	Kai Keng Ang Cuntai Guan Chua, K.S.G. Kok Soon Phua Chuanchu Wang Zheng Yang Chin Longjiang Zhou Ka Yin Tang Joseph, G.J.E. Kuah, C.
Copyright Year	2013
Description	Author affiliation: Tan Tock Seng Hosp., Singapore, Singapore (Chua, K.S.G.; Joseph, G.J.E.; Kuah, C.) \|\| Inst. for Infocomm Res., Agency for Sci., Technol. & Res. (A*STAR), Singapore, Singapore (Kai Keng Ang; Cuntai Guan; Kok Soon Phua; Chuanchu Wang; Zheng Yang Chin; Longjiang Zhou; Ka Yin Tang)
Abstract	Electroencephalogram (EEG) data from performing motor imagery are usually used to calibrate a subject-specific model in Motor Imagery Brain-Computer Interface (MI-BCI). However, the performance of MI is not directly observable by another person. Studies that attempted to address this issue in order to improve subjects with low MI performance had shown that it is feasible to use calibration data from Passive Movement (PM) to detect MI in healthy subjects. This study investigates the feasibility of using calibration data from PM of stroke patients to detect MI. EEG data from 2 calibration runs of MI and PM by a robotic haptic knob, and 1 evaluation run of MI were collected in one session of recording from 34 hemiparetic stroke patients recruited in the clinical study. In each run, 40 trials of MI or PM and 40 trials of the background rest were collected. The off-line run-to-run transfer kappa values from the calibration runs of MI, PM, and combined MI and PM, to the evaluation run of MI were then evaluated and compared. The results showed that calibration using PM (0.392) yielded significantly lower kappa value than the calibration using MI (0.457, p=4.40e-14). The results may be due to a significant disparity between the EEG data from PM and MI in stroke subjects. Nevertheless, the results showed that the calibration using both MI and PM (0.506) yielded significantly higher kappa value than the calibration using MI (0.457, p=9.54e-14). Hence, the results of this study suggest a promising direction to combine calibration data from PM and MI to improve MI detection on stroke.
Starting Page	6603
Ending Page	6606
File Size	512951
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6611069
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Calibration Electroencephalography Brain modeling Data models Robots Haptic interfaces Accuracy
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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