Multi-view Multi-label Fine-grained Emotion Decoding from Human Brain Activity

• URL: http://arxiv.org/abs/2211.02629v1
• Date: Wed, 26 Oct 2022 05:56:54 GMT
• Title: Multi-view Multi-label Fine-grained Emotion Decoding from Human Brain Activity
• Authors: Kaicheng Fu, Changde Du, Shengpei Wang and Huiguang He
• Abstract summary: Decoding emotional states from human brain activity plays an important role in brain-computer interfaces. Existing emotion decoding methods still have two main limitations. We propose a novel multi-view multi-label hybrid model for fine-grained emotion decoding.
• Score: 9.446422699647625
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Decoding emotional states from human brain activity plays an important role in brain-computer interfaces. Existing emotion decoding methods still have two main limitations: one is only decoding a single emotion category from a brain activity pattern and the decoded emotion categories are coarse-grained, which is inconsistent with the complex emotional expression of human; the other is ignoring the discrepancy of emotion expression between the left and right hemispheres of human brain. In this paper, we propose a novel multi-view multi-label hybrid model for fine-grained emotion decoding (up to 80 emotion categories) which can learn the expressive neural representations and predicting multiple emotional states simultaneously. Specifically, the generative component of our hybrid model is parametrized by a multi-view variational auto-encoder, in which we regard the brain activity of left and right hemispheres and their difference as three distinct views, and use the product of expert mechanism in its inference network. The discriminative component of our hybrid model is implemented by a multi-label classification network with an asymmetric focal loss. For more accurate emotion decoding, we first adopt a label-aware module for emotion-specific neural representations learning and then model the dependency of emotional states by a masked self-attention mechanism. Extensive experiments on two visually evoked emotional datasets show the superiority of our method.

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