Motor Imagery EEG Signal Classification Using Distinctive Feature Fusion with Adaptive Structural LASSO

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 12; p. 3755
• Main Authors: Huang, Weihai, Liu, Xinyue, Yang, Weize, Li, Yihua, Sun, Qiyan, Kong, Xiangzeng
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 09.06.2024; MDPI
• Subjects: adaptive LASSO; Algorithms; Brain - diagnostic imaging; Brain - physiology; Brain-Computer Interfaces; brain–computer interface; Classification; common spatial pattern; Deep learning; Electroencephalography; Electroencephalography - methods; Feature selection; Fourier transforms; Humans; Imagination - physiology; Machine learning; motor imagery; Physiology; Signal Processing, Computer-Assisted; Sparsity; Wavelet transforms; electroencephalography; brain–computer interface; motor imagery; common spatial pattern; adaptive LASSO
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24123755

A motor imagery brain–computer interface connects the human brain and computers via electroencephalography (EEG). However, individual differences in the frequency ranges of brain activity during motor imagery tasks pose a challenge, limiting the manual feature extraction for motor imagery classification. To extract features that match specific subjects, we proposed a novel motor imagery classification model using distinctive feature fusion with adaptive structural LASSO. Specifically, we extracted spatial domain features from overlapping and multi-scale sub-bands of EEG signals and mined discriminative features by fusing the task relevance of features with spatial information into the adaptive LASSO-based feature selection. We evaluated the proposed model on public motor imagery EEG datasets, demonstrating that the model has excellent performance. Meanwhile, ablation studies and feature selection visualization of the proposed model further verified the great potential of EEG analysis.