Abstract
In this work, a kernel attention module is presented for the task of EEG-based emotion classification with neural networks . The proposed module utilizes a self-attention mechanism by performing a kernel trick, demanding significantly fewer trainable parameters and computations than standard attention modules. The design also provides a scalar for quantitatively examining the amount of attention assigned during deep feature refinement, hence help better interpret a trained model. Using EEGNet as the backbone model, extensive experiments are conducted on the SEED dataset to assess the module’s performance on within-subject classification tasks compared to other SOTA attention modules. Requiring only one extra parameter, the inserted module is shown to boost the base model’s mean prediction accuracy up to more than 1% across 15 subjects. A key component of the method is the interpretability of solutions, which is addressed using several different techniques, and is included throughout as part of the dependency analysis.
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Notes
- 1.
These are kernel weights in the first depthwise convolutional layer. The shape is of (1, 62) and can be directly associated with the 62 EEG sensor locations on scalp.
- 2.
This might be an interesting coincidence since we also had other cases in our experiments where they do not meet exactly.
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This work was partially supported by the Fundamental Research Funds for the Central Universities, Sun Yat-sen University (22qntd2901).
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Kuang, D., Michoski, C. (2022). KAM - A Kernel Attention Module for Emotion Classification with EEG Data. In: Reyes, M., Henriques Abreu, P., Cardoso, J. (eds) Interpretability of Machine Intelligence in Medical Image Computing. iMIMIC 2022. Lecture Notes in Computer Science, vol 13611. Springer, Cham. https://doi.org/10.1007/978-3-031-17976-1_9
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