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

[2608.02083] A 2-Block Architecture for Real-Time EEG Gait Decoding: A Pilot Study Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2608.02083 (cs) [Submitted on 3 Aug 2026 ( v1 ), last revised 15 Aug 2026 (this version, v2)] Title: A 2-Block Architecture for Real-Time EEG Gait Decoding: A Pilot Study Authors: Shantanu Sarkar , Saurabh Prasad , Jose L. Contreras-Vidal View a PDF of the paper titled A 2-Block Architecture for Real-Time EEG Gait Decoding: A Pilot Study, by Shantanu Sarkar and 1 other authors View PDF HTML (experimental) 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 end-to-end processing time of 70.5~ms (+/-41.5), validating real-time feasibility in this pilot study. Comments: 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 - (Updated/validated Metrics) Subjects: Machine Learning (cs.LG) ; Human-Computer Interaction (cs.HC); Signal Processing (eess.SP) Cite as: arXiv:2608.02083 [cs.LG] (or arXiv:2608.02083v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.02083 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Shantanu Sarkar [ view email ] [v1] Mon, 3 Aug 2026 11:43:31 UTC (1,195 KB) [v2] Sat, 15 Aug 2026 05:37:49 UTC (1,192 KB) Full-text links: Access Paper: View a PDF of the paper titled A 2-Block Architecture for Real-Time EEG Gait Decoding: A Pilot Study, by Shantanu Sarkar and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-08 Change to browse by: cs cs.HC eess eess.SP References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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