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Stratified domain generalized motor imagery electroencephalogram classification via deep mixture distribution representation learning
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DOI：10.1016/j.irbm.2026.100952.png)
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Abstract
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• A stratified domain generalization is proposed for cross-subject MI-EEG decoding. • We learned a mixture distribution among multiple subjects centroid aligned EEG samples. • We jointly learned a weighted loss to maximize the generalization for unseen subjects.