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

arXiv: https://arxiv.org/abs/2603.29205 · HTML: https://mlsanity.com/paper/2603.29205/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2026.

## Built on by (most cited first, 1 of 1)

- [Adaptive Forgetting for Nonstationary Optimization: Towards Robust EEG Decoding](https://mlsanity.com/paper/2609.24233/) (2026)

## Abstract (arXiv)

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.