{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T13:13:50Z","timestamp":1788527630785,"version":"build-2803163510"},"reference-count":48,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:00:00Z","timestamp":1788480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Healthcare of the Russian Federation","award":["125022602910-2"],"award-info":[{"award-number":["125022602910-2"]}]},{"award":["125022602910-2"],"award-info":[{"award-number":["125022602910-2"]}],"id":[{"id":"https:\/\/ror.org\/01p8ehb87","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Among the various brain\u2013computer interface (BCI) modifications used in post-stroke rehabilitation, BCI systems combined with functional electrical stimulation (FES) are considered the most effective. As a preliminary step toward optimizing such systems for clinical application, it remains unclear whether using electroencephalography (EEG) alone versus a hybrid EEG and functional near-infrared spectroscopy (fNIRS) approach affects real-time three-class BCI\u2013FES control performance in healthy individuals. In a blind randomized study, 16 healthy volunteers completed five BCI\u2013FES training sessions across three days. In one group, FES of wrist extensor muscles was driven by a hybrid EEG\u2013fNIRS classifier; in the other, by EEG only. Classification accuracy, sense of agency, attention, and physical comfort were assessed. No statistically significant between-group differences were found in any outcome measure (p &gt; 0.05). Median real-time three-class classification recall was 53.5% in the hybrid group and 57.3% in the EEG-only group. The median agency score reached approximately 75% of the maximum possible value in both groups. Simulation analysis showed comparable accuracy for unimodal fNIRS-only and EEG-only classifiers. Genetic algorithm-based channel selection identified C3 and C4 as the most informative EEG channels, while optimal fNIRS placement required individual optimization. Within the constraints of the classification and fusion pipeline used here, these findings suggest that signal acquisition modality does not significantly influence BCI\u2013FES performance or sense of agency in healthy subjects. The complete EEG\u2013fNIRS dataset is publicly available through NITRC.<\/jats:p>","DOI":"10.3390\/s26175617","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T12:20:19Z","timestamp":1788524419000},"page":"5617","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EEG vs. Hybrid EEG\u2013fNIRS BCI for FES Control in Healthy Subjects: A Blind Randomized Study and an Open Dataset"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7826-5135","authenticated-orcid":false,"given":"Olesya","family":"Mokienko","sequence":"first","affiliation":[{"name":"Department of Neuro-computer Interfaces, Pirogov Russian National Research Medical University, Moscow 117513, Russia"},{"name":"Institute for Neurorehabilitation and Restorative Technologies, Russian Center of Neurology and Neurosciences, Moscow 125310, 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