Adaptation of Common Spatial Patterns based on mental fatigue for motor-imagery BCI. (April 2020)

• Record Type: Journal Article
• Authors: Talukdar, Upasana; Hazarika, Shyamanta M.; Gan, John Q.
• Is Part Of: Biomedical signal processing and control. Volume 58(2020)
• Publication Date: 2020-04
• Subjects: Brain–Computer Interface -- Motor imagery -- Electroencephelogram -- Common Spatial Patterns -- Adaptation -- Mental fatigue; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.101829
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Common Spatial Pattern (CSP) is the most popular method for extracting features from electroencephalogram (EEG) signals in motor imagery (MI) based Brain–Computer Interfaces (BCI). Due to the non-stationary nature of EEG signals, the CSP computed on the training data may not be optimal for the evaluation data. This paper proposes an adaptive scheme based on Linear Discriminant Analysis (LDA) active learning for the CSP depending on the mental fatigue of the user. Experimental results show higher separability of features extracted with adaptive CSP as compared to that with conventional CSP. Abstract: Common Spatial Pattern (CSP) is the most popular method in motor imagery (MI) based Brain–Computer Interfaces (BCI) for extracting features from electroencephalogram (EEG) signals. Due to the non-stationary nature of EEG signals, the CSP computed on the training data may not be optimal for the evaluation data. One of the major causes of such non-stationarity is the change in user's cognitive state due to fatigue, frustration, low arousal level, etc. This paper proposes an adaptive scheme for the CSP based on the mental fatigue of the user. The proposed method uses Linear Discriminant Analysis (LDA) active learning to adapt the CSP. Breaking ties criterion is used for selecting samples from the evaluation data. The separability of MI EEG features extracted with the proposed adaptive CSP has been compared with that of conventional CSP in terms of three separability metrics: Davies Bouldin Index (DBI), Fisher's Score (FS) and Dunn's Index (DI). Experimental results show significantly higher separability of features extracted with adaptive CSP as compared to that with conventional CSP.