Why the study?
Deep learning models remain mostly black boxes in EEG-related tasks, where understanding the reasons behind predictions is crucial for assessing trust and improving performance.
Integrating brain science knowledge with interpretability analysis results in the learning process improves EEG-based emotion recognition accuracy.
Supports knowledge integration for EEG emotion models; leaves open clinical translation in cardiovascular research.
Despite achieving success in many domains, deep learning models remain mostly black boxes, especially in electroencephalogram (EEG)-related tasks. Meanwhile, understanding the reasons behind model predictions is quite crucial in assessing trust and performance promotion in EEG-related tasks. In this work, we explore the use of representative interpretable models to analyze the learning behavior of convolutional neural networks (CNN) in EEG-based emotion recognition. According to the interpretable analysis, we find that similar features captured by our model and state-of-the-art model are consistent with previous brain science findings. Next, we propose a new model by integrating brain science knowledge with the interpretability analysis results in the learning process. Our knowledge-integrated model achieves better recognition accuracy on standard EEG-based recognition datasets.
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Zhang et al. (2023) studied this question.
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