BiMoE: Brain-Inspired Experts for EEG-Dominant Affective State Recognition

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Abstract: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: this https URL.

Comments: Accepted by ICME 2026

Subjects:

Human-Computer Interaction (cs.HC)

Cite as: arXiv:2603.29205 [cs.HC]

(or arXiv:2603.29205v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2603.29205

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Hongyu Zhu [view email] [v1] Tue, 31 Mar 2026 03:17:45 UTC (2,025 KB)