State-Flow Coordinated Representation for MI-EEG Decoding

TL;DR

This paper introduces StaFlowNet, a dual-branch neural network that separately extracts and coordinates global state and temporal flow information from MI-EEG signals, improving decoding accuracy.

Contribution

The novel state-flow coordinated architecture explicitly separates and dynamically integrates state and flow features, advancing MI-EEG decoding performance.

Findings

StaFlowNet outperforms existing methods on three public datasets.

The state-modulated flow module enhances feature discriminability.

Ablation studies confirm the importance of the state-flow coordination mechanism.

Abstract

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.