A 2-Block Architecture for Real-Time EEG Gait Decoding: A Pilot Study

Abstract:Closed-loop lower-limb exoskeleton control via Electroencephalography (EEG) remains limited by motion artifacts, low signal-to-noise ratio, and binary gait formulations that fail to capture full cortical gait complexity. We propose a 2-block Brain-Computer Interface (BCI) architecture: a trainable session-specific Feature Extraction Block with real-time artifact suppression and multi-domain feature extraction, coupled with a Decoder Block built on a novel Polynomial Time-Varying Layer (PolyTVL)+LSTM for four-state gait classification (Stand, Initiate, Execute, Terminate). Ablation confirmed v01 (PolyTVL+LSTM) outperformed all variants (validation MCC: 0.435, gap: 0.187), with consistent EEG feature discriminability across ROIs and sub-bands (p<0.05). Closed-loop deployment with v01 achieved 55.3% (Rex-assisted) and 52.7% (volitional) gait initiation success, with a mean prediction time of 70.5~ms (+/-41.5), validating real-time feasibility in this pilot study.

Accepted for publication in the 2026 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2026), September 28-October 1, 2026, Atlanta, GA, USA. Camera-ready version

Machine Learning (cs.LG); Human-Computer Interaction (cs.HC); Signal Processing (eess.SP)

arXiv:2608.02083 [cs.LG]

(or arXiv:2608.02083v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.02083 arXiv-issued DOI via DataCite (pending registration)