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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.21148 |
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| _version_ | 1866908795910750208 |
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| author | Zhao, Ziyi Zhou, Jinzhao Jiang, Xiaowei Cao, Beining Ma, Wenhao Shen, Yang Li, Ren Wang, Yu-Kai Lin, Chin-teng |
| author_facet | Zhao, Ziyi Zhou, Jinzhao Jiang, Xiaowei Cao, Beining Ma, Wenhao Shen, Yang Li, Ren Wang, Yu-Kai Lin, Chin-teng |
| contents | Decoding linguistic information from electroencephalography (EEG) remains challenging due to the brain's distributed and nonlinear organization. We present BrainStack, a functionally guided neuro-mixture-of-experts (Neuro-MoE) framework that models the brain's modular functional architecture through anatomically partitioned expert networks. Each functional region is represented by a specialized expert that learns localized neural dynamics, while a transformer-based global expert captures cross-regional dependencies. A learnable routing gate adaptively aggregates these heterogeneous experts, enabling context-dependent expert coordination and selective fusion. To promote coherent representation across the hierarchy, we introduce cross-regional distillation, where the global expert provides top-down regularization to the regional experts. We further release SilentSpeech-EEG (SS-EEG), a large-scale benchmark comprising over 120 hours of EEG recordings from 12 subjects performing 24 silent words, the largest dataset of its kind. Experiments demonstrate that BrainStack consistently outperforms state-of-the-art models, achieving superior accuracy and generalization across subjects. Our results establish BrainStack as a functionally modular, neuro-inspired MoE paradigm that unifies neuroscientific priors with adaptive expert routing, paving the way for scalable and interpretable brain-language decoding. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21148 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BrainStack: Neuro-MoE with Functionally Guided Expert Routing for EEG-Based Language Decoding Zhao, Ziyi Zhou, Jinzhao Jiang, Xiaowei Cao, Beining Ma, Wenhao Shen, Yang Li, Ren Wang, Yu-Kai Lin, Chin-teng Artificial Intelligence Decoding linguistic information from electroencephalography (EEG) remains challenging due to the brain's distributed and nonlinear organization. We present BrainStack, a functionally guided neuro-mixture-of-experts (Neuro-MoE) framework that models the brain's modular functional architecture through anatomically partitioned expert networks. Each functional region is represented by a specialized expert that learns localized neural dynamics, while a transformer-based global expert captures cross-regional dependencies. A learnable routing gate adaptively aggregates these heterogeneous experts, enabling context-dependent expert coordination and selective fusion. To promote coherent representation across the hierarchy, we introduce cross-regional distillation, where the global expert provides top-down regularization to the regional experts. We further release SilentSpeech-EEG (SS-EEG), a large-scale benchmark comprising over 120 hours of EEG recordings from 12 subjects performing 24 silent words, the largest dataset of its kind. Experiments demonstrate that BrainStack consistently outperforms state-of-the-art models, achieving superior accuracy and generalization across subjects. Our results establish BrainStack as a functionally modular, neuro-inspired MoE paradigm that unifies neuroscientific priors with adaptive expert routing, paving the way for scalable and interpretable brain-language decoding. |
| title | BrainStack: Neuro-MoE with Functionally Guided Expert Routing for EEG-Based Language Decoding |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.21148 |