Journal
ELECTRONICS
Volume 9, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/electronics9040690
Keywords
motor imagery brain-computer interface; predictor; resting state
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Funding
- Institute of Information and communications Technology Planning and Evaluation (IITP) - Korea government [2017-0-00451]
- (Development of BCI based Brain and Cognitive Computing Technology for Recognizing User's Intentions using Deep Learning)
- Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea [2017-0-00451-004] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Motor-imagery brain-computer interface (MI-BCI) is a technique that manipulates external machines using brain activities, and is highly useful to amyotrophic lateral sclerosis patients who cannot move their limbs. However, it is reported that approximately 15-30% of users cannot modulate their brain signals, which results in the inability to operate motor imagery BCI systems. Thus, advance prediction of BCI performance has drawn researchers' attention, and some predictors have been proposed using the alpha band's power, as well as other spectral bands' powers, or spectral entropy from resting state electroencephalography (EEG). However, these predictors rely on a single state alone, such as the eyes-closed or eyes-open state; thus, they may often be less stable or unable to explain inter-/intra-subject variability. In this work, a modified predictor of MI-BCI performance that considered both brain states (eyes-open and eyes-closed resting states) was investigated with 41 online MI-BCI session datasets acquired from 15 subjects. The results showed that our proposed predictor and online MI-BCI classification accuracy were positively and highly significantly correlated (r = 0.71, p < 0.1 x 10(-7)), which indicates that the use of multiple brain states may yield a more robust predictor than the use of a single state alone.
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