A Decade-Long Systematic Review of EEG-Driven Gait-Intention Interfaces for Lower-Limb Rehabilitation and Prosthetics

Abstract:

Over the past decade, noninvasive brain-computer interfaces (BCIs) leveraging electroencephalography(EEG) to decode gait intention have matured from proof-of-concept studies to nearclinicalimplementations. We systematically reviewed 55 studies (January 2015-April 2024) usingPRISMA guidelines, focusing on neurophysiological markers (MRCPs, ERD/ERS, high-γ), signalprocessing pipelines (artifact suppression, time-frequency transforms), machine-learning classifiers(CSP-SVM, ERD SVM), and deep-learning frameworks (spatio-spectral CNNs, LSTM RNNs). Acrossstudies, median classification accuracy rose from 75% (2015-2018) to 87% (2021-2024), while detectionlatency fell below 200ms. Innovations include enhancing intention detection with emotionevokingmusic stimuli (up to early ERD and improved accuracy), decoding pediatric gait kinematicswith state-space models (r = 0.71 hip, 0.59 knee), and session-to-session transfer learning withoutrecalibration (≤4% performance drop). Challenges remain in artifact mitigation, small sample sizes,and limited multi-centre trials. We propose open, standardized datasets, transfer-learning pipelines,and larger clinical validations to accelerate translation.

• Periodical: Applied Mechanics and Materials (Volume 929)
• Pages: 107-118
• DOI: https://doi.org/10.4028/p-rpE8vv
• Online since: December 2025
• Authors: Diomar Enrique Rodriguez-Obregon*, Héctor A. Moreno, Isela G. Carrera, Lizeth Lopez-Lopez
• Keywords: Brain Computer Interface, Electroencephalography, Exoskeleton, Lower-Limb Rehabilitation, Prosthesis

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