title: A minimal set of electrodes for motor imagery BCI to control an assistive device in chronic stroke subjects: a multi-session study.
abstract: The brain-computer interface (BCI) system has been developed to assist people with motor disability. To make the system more user-friendly, it is a challenge to reduce the electrode preparation time and have a good reliability. This study aims to find a minimal set of electrodes for an individual stroke subject for motor imagery to control an assistive device using functional electrical stimulation for 20 sessions with accuracy higher than 90%. The characteristics of this minimal electrode set were evaluated with two popular algorithms: Fisher's criterion and support-vector machine recursive feature elimination (SVM-RFE). The number of calibration sessions for channel selection required for robust control of these 20 sessions was also investigated. Five chronic stroke patients were recruited for the study. Our results suggested that the number of calibration sessions for channel selection did not have a significant effect on the classification accuracy. A performance index devised in this study showed that one training day with 12 electrodes using the SVM-RFE method achieved the best balance between the number of electrodes and accuracy in the 20-session data. Generally, 8-36 channels were required to maintain accuracy higher than 90% in 20 BCI training sessions for chronic stroke patients.
authors: Department of Health Technology and Informatics, Hong Kong Polytechnic University, Kowloon, Hong Kong. teris.tamwk@polyu.edu.hk
authors: Wing-Kin
authors: WK
authors: Tam
authors: Kai-yu
authors: KY
authors: Tong
authors: Fei
authors: F
authors: Meng
authors: Shangkai
authors: S
authors: Gao
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D002138:Calibration; D002540:Cerebral Cortex / Q000502:physiology; D002908:Chronic Disease; D003627:Data Interpretation, Statistical; D004558:Electric Stimulation; D004566:Electrodes; D004569:Electroencephalography; D005260:Female; D007839:Functional Laterality / Q000502:physiology; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008297:Male; D008875:Middle Aged; D009068:Movement / Q000502:physiology; D009862:Online Systems; D011597:Psychomotor Performance / Q000502:physiology; D012656:Self-Help Devices; D000071939:Stroke Rehabilitation; D060388:Support Vector Machine; D014584:User-Computer Interface