Adaptive feature extraction in EEG-based motor imagery BCI: tracking mental fatigue

• Published in: Journal of neural engineering Vol. 17; no. 1; p. 016020
• Main Authors: Talukdar, Upasana, Hazarika, Shyamanta M, Gan, John Q
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 06.01.2020
• Subjects: adaptation; BCI; Brain-Computer Interfaces; common spatial pattern; EEG; Electroencephalography - methods; Humans; Imagination - physiology; mental fatigue; Mental Fatigue - diagnosis; Mental Fatigue - physiopathology; Mental Fatigue - psychology; motor imagery; Movement - physiology; Photic Stimulation - methods; Unsupervised Machine Learning
• ISSN: 1741-2560; 1741-2552; 1741-2552
• DOI: 10.1088/1741-2552/ab53f1

Objective. Electroencephalogram (EEG) signals are non-stationary. This could be due to internal fluctuation of brain states such as fatigue, frustration, etc. This necessitates the development of adaptive brain-computer interfaces (BCI) whose performance does not deteriorate significantly with the adversary change in the cognitive state. In this paper, we put forward an unsupervised adaptive scheme to adapt the feature extractor of motor imagery (MI) BCIs by tracking the fatigue level of the user. Approach. Eleven subjects participated in the study during which they accomplished MI tasks while self-reporting their perceived levels of mental fatigue. Out of the 11 subjects, only six completed the whole experiment, while the others quit in the middle because of experiencing high fatigue. The adaptive feature extractor is attained through the adaptation of the common spatial patterns (CSP), one of the most popular feature extraction algorithms in EEG-based BCIs. The proposed method was analyzed in two ways: offline and in near real-time. The separability of the MI EEG features extracted by the proposed adaptive CSP (ADCSP) has been compared with that by the conventional CSP (C-CSP) and another CSP based adaptive method (ACSP) in terms of: Davies Bouldin index (DBI), Fisher score (FS) and Dunn's index (DI). Main results. Experimental results show significant improvement in the separability of MI EEG features extracted by ADCSP as compared to that by C-CSP and ACSP. Significance. Collectively, the results of the experiments in this study suggest that adapting CSP based on mental fatigue can improve the class separability of MI EEG features.