Offline and simulated-online fNIRS decoding of ankle movements for prospective exo-ankle control: a pilot study in healthy adults and ankle-impaired patients

Abstract

This study developed and evaluated a non-invasive brain–computer interface (BCI) framework that decodes functional near-infrared spectroscopy (fNIRS) signals from the motor cortex to generate offline and simulated-online exo-ankle control commands, as a proof-of-concept step toward future prospective exo-ankle control. fNIRS data were recorded from 20 healthy participants and seven patients with ankle motor impairment using a PsychoPy-based paradigm of rest, dorsiflexion, and plantar flexion. Following the literature, after preprocessing, an evaluation of different combinations of six statistical features and four classifiers, namely, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machine (SVM), and k-nearest neighbor (kNN) was conducted. Under leave-one-subject-out (LOSO) cross-validation across 20 healthy participants, SVM achieved the mean offline classification accuracy of 72.50% ± 13.8% followed by kNN (65.83% ± 13.5%), LDA (63.75% ± 16.1%), and QDA (60.42% ± 17.7%). A Friedman test indicated a significant overall difference among classifiers ( p = 0.0311). Simulated-online implementation used SVM with a sliding window approach with step sizes of 0.5, 1, and 1.5 s under participant-specific leave-one-trial-out validation. The results showed a highest mean accuracy of 62.2% ± 14.1% at a 1.5-s step size. The patient group showed a lower mean accuracy of 57.23% ± 5% using SVM under leave-one-trial-out cross-validation. Together, these offline and simulated-online results establish proof-of-concept feasibility for fNIRS-based exo-ankle control, providing a foundation for future prospective rehabilitation applications.