State-Flow Coordinated Representation for MI-EEG Decoding
This addresses the challenge of unstable learning and sub-optimal decoding for MI-EEG applications, representing an incremental improvement in domain-specific deep learning methods.
The paper tackled the problem of sub-optimal performance in Motor Imagery EEG decoding by proposing StaFlowNet, which coordinates state and flow information, resulting in significant outperformance over state-of-the-art methods on three public datasets.
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.