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Journal of Computational Neuroscience

Erschienen in:

29.11.2018

Motor imagery and mental fatigue: inter-relationship and EEG based estimation

verfasst von: Upasana Talukdar, Shyamanta M. Hazarika, John Q. Gan

Erschienen in: Journal of Computational Neuroscience | Ausgabe 1/2019

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Abstract

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.

Vorheriger Artikel Convolutional neural network models of V1 responses to complex patterns

Nächster Artikel Linear feature projection-based real-time decoding of limb state from dorsal root ganglion recordings

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Titel: Motor imagery and mental fatigue: inter-relationship and EEG based estimation
verfasst von: Upasana Talukdar
Shyamanta M. Hazarika
John Q. Gan
Publikationsdatum: 29.11.2018
Verlag: Springer US
Erschienen in: Journal of Computational Neuroscience / Ausgabe 1/2019
Print ISSN: 0929-5313
Elektronische ISSN: 1573-6873
DOI: https://doi.org/10.1007/s10827-018-0701-0

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