Offline and simulated-online fNIRS decoding of ankle movements for prospective exo-ankle control: a pilot study in healthy adults and ankle-impaired patients
- 1Department of Mechatronics and Biomedical Engineering, Air University , ,
- 2Faculty of Rehabilitation & Allied Health Sciences, Riphah International University , ,
- 3Department of Biomedical Engineering, The Chinese University of Hong Kong , , ,
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.
Related
- Simulated transcranial ultrasound of human language cortex: verifying heard speech needs far less data and signal than decoding it
- Fibrinolysis is impaired in patients with primary immune thrombocytopenia
- Decoding candidemia in critically ill patients: unsupervised clustering identifies three unique phenotypes
- Safety of lecanemab in real-world patients with Alzheimer’s disease: a prospective, observational case series supported by FAERS signal analysis
- Point-of-care hearing screening using consumer wireless earbuds in patients with USH2A-associated retinal disease: a prospective pilot feasibility study
BibTeX
@article{ahmad2026offline,
title = {{Offline and simulated-online fNIRS decoding of ankle movements for prospective exo-ankle control: a pilot study in healthy adults and ankle-impaired patients}},
author = {Naqash Ahmad and Hammad Nazeer and Noman Naseer and Mirza Obaid Baig and Sadia Shakil and Kai-yu Tong},
journal = {Frontiers in neuroergonomics},
year = {2026},
month = oct,
doi = {10.3389/fnrgo.2026.1726286},
url = {https://europepmc.org/article/PMC/PMC13719470},
note = {CC BY},
}
RIS
TY - JOUR
TI - Offline and simulated-online fNIRS decoding of ankle movements for prospective exo-ankle control: a pilot study in healthy adults and ankle-impaired patients
AU - Naqash Ahmad
AU - Hammad Nazeer
AU - Noman Naseer
AU - Mirza Obaid Baig
AU - Sadia Shakil
AU - Kai-yu Tong
PY - 2026
DA - 2026/10/16
JO - Frontiers in neuroergonomics
DO - 10.3389/fnrgo.2026.1726286
UR - https://europepmc.org/article/PMC/PMC13719470
AB - 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.
SN - 2673-6195
ER -