Motor imagery and mental fatigue: inter-relationship and EEG based estimation
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Even though it has long been felt that psychological state influences the performance of brain-computer interfaces (BCI), formal analysis to support this hypothesis has been scant. This study investigates the inter-relationship between motor imagery (MI) and mental fatigue using EEG: a. whether prolonged sequences of MI produce mental fatigue and b. whether mental fatigue affects MI EEG class separability. Eleven participants participated in the MI experiment, 5 of which quit in the middle because of experiencing high fatigue. The growth of fatigue was monitored using the Kernel Partial Least Square (KPLS) algorithm on the remaining 6 participants which shows that MI induces substantial mental fatigue. Statistical analysis of the effect of fatigue on motor imagery performance shows that high fatigue level significantly decreases MI EEG separability. Collectively, these results portray an MI-fatigue inter-connection, emphasizing the necessity of developing adaptive MI BCI by tracking mental fatigue.
KeywordsMotor imagery Mental fatigue Brain Computer Interface EEG
Financial support from MHRD as Centre of Excellence on Machine Learning Research and Big Data Analysis is gratefully acknowledged. Assistance received from DST-UKEIRI Project: DST/INT/UK/P-91/2014 is also acknowledged.
Compliance with Ethical Standards
Conflict of interest
The authors declare that they have no conflict of interest.
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