ArticleSensors (Basel, Switzerland)2024
Motor Imagery EEG Signal Classification Using Distinctive Feature Fusion with Adaptive Structural LASSO.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Effects of motor imagery brain-computer interface task on quantitative EEG features in patients with prolonged disorders of consciousness.Frontiers in neuroscience · 2026Article
- Classification of finger movements through optimal EEG channel and feature selection.Frontiers in human neuroscience · 2025Article
- EEG channel and feature investigation in binary and multiple motor imagery task predictions.Frontiers in human neuroscience · 2024Article
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Authors and funding
6 authors.
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Abstract
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.
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