BiMoE: Brain-Inspired Experts for EEG-Dominant Affective State Recognition
This work addresses multimodal sentiment analysis for brain-computer interface systems, offering incremental improvements in accuracy.
The paper tackled the problem of multimodal sentiment analysis by integrating EEG and peripheral physiological signals, proposing BiMoE to address challenges like overlooking region-specific EEG characteristics and ineffective feature fusion, resulting in average accuracy improvements of 0.87% to 5.19% on DEAP and DREAMER datasets.
Multimodal Sentiment Analysis (MSA) that integrates Electroencephalogram (EEG) with peripheral physiological signals (PPS) is crucial for the development of brain-computer interface (BCI) systems. However, existing methods encounter three major challenges: (1) overlooking the region-specific characteristics of affective processing by treating EEG signals as homogeneous; (2) treating EEG as a black-box input, which lacks interpretability into neural representations;(3) ineffective fusion of EEG features with complementary PPS features. To overcome these issues, we propose BiMoE, a novel brain-inspired mixture of experts framework. BiMoE partitions EEG signals in a brain-topology-aware manner, with each expert utilizing a dual-stream encoder to extract local and global spatiotemporal features. A dedicated expert handles PPS using multi-scale large-kernel convolutions. All experts are dynamically fused through adaptive routing and a joint loss function. Evaluated under strict subject-independent settings, BiMoE consistently surpasses state-of-the-art baselines across various affective dimensions. On the DEAP and DREAMER datasets, it yields average accuracy improvements of 0.87% to 5.19% in multimodal sentiment classification. The code is available at: https://github.com/HongyuZhu-s/BiMo.