State-Flow Coordinated Representation for MI-EEG Decoding
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866913018600751104 |
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| author | Cai, Guoqing Huang, Shoulin Ma, Ting |
| author_facet | Cai, Guoqing Huang, Shoulin Ma, Ting |
| contents | Motor Imagery (MI) Electroencephalography (EEG) signals contain two crucial and complementary types of information: state information, which captures the global context of the task, and flow information, which captures fine-grained temporal dynamics. However, existing deep decoding models typically focus on only one of these information streams, resulting in unstable learning and sub-optimal performance. To address this, we propose the State-Flow Coordinated Network (StaFlowNet), a novel architecture that explicitly separates and coordinates state and flow information. We first employ a dual-branch design to extract the global state vector and temporal flow features separately. Critically, a novel state-modulated flow module is proposed to dynamically refine the learning of flow information. This modulated mechanism effectively integrates global context with fine-grained dynamics, thereby significantly enhancing task discriminability and decoding performance. Experiments on three public MI-EEG datasets demonstrate that StaFlowNet significantly outperforms state-of-the-art methods. Ablation studies further confirm that the state-modulated mechanism plays a crucial role in enhancing feature discriminability and overall performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08157 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | State-Flow Coordinated Representation for MI-EEG Decoding Cai, Guoqing Huang, Shoulin Ma, Ting Human-Computer Interaction Motor Imagery (MI) Electroencephalography (EEG) signals contain two crucial and complementary types of information: state information, which captures the global context of the task, and flow information, which captures fine-grained temporal dynamics. However, existing deep decoding models typically focus on only one of these information streams, resulting in unstable learning and sub-optimal performance. To address this, we propose the State-Flow Coordinated Network (StaFlowNet), a novel architecture that explicitly separates and coordinates state and flow information. We first employ a dual-branch design to extract the global state vector and temporal flow features separately. Critically, a novel state-modulated flow module is proposed to dynamically refine the learning of flow information. This modulated mechanism effectively integrates global context with fine-grained dynamics, thereby significantly enhancing task discriminability and decoding performance. Experiments on three public MI-EEG datasets demonstrate that StaFlowNet significantly outperforms state-of-the-art methods. Ablation studies further confirm that the state-modulated mechanism plays a crucial role in enhancing feature discriminability and overall performance. |
| title | State-Flow Coordinated Representation for MI-EEG Decoding |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2604.08157 |