Optimal channel and frequency band‐based feature selection for motor imagery electroencephalogram classification

Abstract

Common spatial pattern (CSP) is a widely adopted method for electroencephalogram (EEG) feature extraction in brain-computer interface (BCI) based on motor imagery. Bandpass-filtering EEG into several subbands related to brain activity tasks is an effective approach to improve the performance of CSP based algorithm. However, this approach tends to suffer the over-fitting problem because of the increase in feature dimension. Therefore, we proposed an optimal channel and frequency band-based CSP feature selection method in this paper. Firstly, the correlation coefficient was calculated to select the optimal channels, and these channels were bandpass-filtered into multiple overlapping subbands. The subbands with higher power spectrum density were chosen for CSP feature extraction. Next, the pair-wise relevance was utilized to remove subband features with less difference. And then the screened subband features were combined with features extracted from the broadband signal. The Fisher ratio was exploited to carry out further feature selection. Finally, a support vector machine (SVM) was trained to classify the selected CSP features. An experimental study was implemented on BCI competition III dataset IVa and BCI competition IV dataset 1. The average classification accuracy reached 89.33% and 84.08%, which indicated the rationality and effectiveness of the proposed method.

Algorithm contest of motor imagery BCI in the World Robot Contest 2022: A survey

“…Therefore, using more EEG channels does not guarantee performance improvement [20]. Many studies related to channel selection are conducted to remove redundant channels irrelevant to the MI task [21][22][23][24][25].…”

“…Therefore, using more EEG channels does not guarantee performance improvement [20]. Many studies related to channel selection are conducted to remove redundant channels irrelevant to the MI task [21 –25]. Based on the ERD/ERS of SMRs, signal filtering selects the most valuable frequency range for the MI tasks [26, 27].…”

“…The working principle of the EEG-based MI-BCIs is the changes in SMRs. Based on neurophysiological research in the sensorimotor cortex, MI alters the mu (8-12 Hz) and beta (18)(19)(20)(21)(22)(23)(24)(25)(26) rhythms [16]. The energy modulation of brain rhythms due to an event in a specific frequency range is called eventrelated desynchronization (ERD)/event-related synchronization (ERS) [17].…”

Survey on the research direction of EEG-based signal processing

“…Common Spatial Pattern (CSP) is a space domain filtering algorithm used for binary classification tasks. CSP extracts the spatial distribution components of each class of the multi-channel EEG signal and seeks the best projection direction to maximize the variance of one class and minimize that of the other class (Meng et al, 2022 ). Since CSP maximizes the difference among EEG signals, it is more capable of mining the features of EEG signals.…”

The effects of layer-wise relevance propagation-based feature selection for EEG classification: a comparative study on multiple datasets

“…Specifically, Zhang et al ( 2021a ) have extracted time-frequency features through wavelet transformation and selected crucial EEG channels via squeeze-and-excitation blocks. In addition, recent studies focusing on EEG feature selection have been actively considering various combinations of the spatial, temporal, and spectral domains through a range of approaches (Abbas and Khan, 2018 ; Liu et al, 2022 ; Sadiq et al, 2022 ; Tang et al, 2022 ; Luo, 2023 ; Meng et al, 2023 ).…”