Mirror contrastive loss based sliding window transformer for subject-independent motor imagery based EEG signal recognition

TL;DR

This paper introduces MCL-SWT, a novel deep learning model that uses mirror contrastive loss and a sliding window transformer to improve subject-independent EEG motor imagery recognition, achieving higher accuracy than existing methods.

Contribution

The paper proposes a new mirror contrastive loss and a temporal sliding window transformer for EEG signal recognition, enhancing spatial sensitivity and temporal feature extraction in a subject-independent setting.

Findings

MCL-SWT outperforms state-of-the-art models in accuracy.

Mirror contrastive loss improves spatial sensitivity to ERD.

The model achieves 66.48% and 75.62% accuracy on two datasets.

Abstract

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.