Mirror contrastive loss based sliding window transformer for subject-independent motor imagery based EEG signal recognition
While deep learning models have been extensively utilized in motor imagery based EEG signal recognition, they often operate as black boxes. Motivated by neurological findings indicating that the mental imagery of left or right-hand movement induces event-related desynchronization (ERD) in the contra...
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Zusammenfassung: | While deep learning models have been extensively utilized in motor imagery
based EEG signal recognition, they often operate as black boxes. Motivated by
neurological findings indicating that the mental imagery of left or right-hand
movement induces event-related desynchronization (ERD) in the contralateral
sensorimotor area of the brain, we propose a Mirror Contrastive Loss based
Sliding Window Transformer (MCL-SWT) to enhance subject-independent motor
imagery-based EEG signal recognition. Specifically, our proposed mirror
contrastive loss enhances sensitivity to the spatial location of ERD by
contrasting the original EEG signals with their mirror counterparts-mirror EEG
signals generated by interchanging the channels of the left and right
hemispheres of the EEG signals. Moreover, we introduce a temporal sliding
window transformer that computes self-attention scores from high temporal
resolution features, thereby improving model performance with manageable
computational complexity. We evaluate the performance of MCL-SWT on
subject-independent motor imagery EEG signal recognition tasks, and our
experimental results demonstrate that MCL-SWT achieved accuracies of 66.48% and
75.62%, surpassing the state-of-the-art (SOTA) model by 2.82% and 2.17%,
respectively. Furthermore, ablation experiments confirm the effectiveness of
the proposed mirror contrastive loss. A code demo of MCL-SWT is available at
https://github.com/roniusLuo/MCL_SWT. |
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DOI: | 10.48550/arxiv.2409.00130 |