Work

EEG-to-gait decoding via phase-aware representation learning

Year: 2026

Type: article

Abstract: Accurate decoding of lower-limb motion from EEG signals is essential for advancing brain-computer interface (BCI) applications in movement intent recognition and control. This study presents NeuroDyGait, a two-stage, phase-aware EEG-to-gait decoding framework that explicitly models temporal continuity and domain relationships. To address challenges of causal, phase-consistent prediction and cross-subject variability, Stage I learns semantically aligned EEG-motion embeddings via relative contrastive learning with a cross-attention-based metric, while Stage II performs domain relation-aware decoding through dynamic fusion of session-specific heads. Comprehensive experiments on two benchmark datasets (GED and FMD) show substantial gains over baselines, including a recent 2025 model EEG2GAIT. The framework generalizes to unseen subjects and maintains inference latency below 5 ms per window, satisfying real-time BCI requirements. Visualization of learned attention and phase-specific cortical saliency maps further reveals interpretable neural correlates of gait phases. Future extensions will target rehabilitation populations and multimodal integration.

Source: Neural Networks

DOI: 10.1016/j.neunet.2026.108608

Authors: Xi Fu, Weibang Jiang, Rui Liu, Gernot R. Müller-Putz, Cuntai Guan

Institutions: Nanyang Technological University, Shanghai Jiao Tong University, Graz University of Technology, Centre of AI in Medicine

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,110