2026/08/03 by Shantanu Sarkar, Saurabh Prasad, Jose L. Contreras-Vidal
Computer Science · Engineering · #cs.LG #cs.HC #eess.SP
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
arxiv created 2026/08/03 · arxiv updated 2026/08/04
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.