Abstract
Electroencephalogram (EEG) based emotion recognition has received considerable attention from many researchers. Methods based on deep learning have made significant progress. However, most of the existing solutions still need to use manually extracted features as the input to train the network model. Neuroscience studies suggest that emotion reveals asymmetric differences between the left and right hemispheres of the brain. Inspired by this fact, we proposed a hemispheric asymmetry measurement network (HAMNet) to learn discriminant features for emotion classification tasks. Our network is end-to-end and reaches the average accuracy of 96.45%, which achieves the state-of-the-art (SOTA) performance. Moreover, the visualization and analysis of the learned features provides a possibility for neuroscience to study the mechanism of emotion.
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This work is supported by National Natural Science Foundation of China grant 61876147.
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Yan, R., Lu, N., Niu, X., Yan, Y. (2022). Hemispheric Asymmetry Measurement Network for Emotion Classification. In: Deng, W., et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. https://doi.org/10.1007/978-3-031-20233-9_31
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DOI: https://doi.org/10.1007/978-3-031-20233-9_31
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