France

A team in France and the United States has released NETBCI, a longitudinal multimodal dataset pairing magnetoencephalography (MEG) and electroencephalography (EEG) from 19 healthy subjects across 4 sessions performed on 4 different days, built to study how brain networks reorganize during brain-computer interface training. Controlling a BCI remains a learned skill that a non-negligible proportion of users never acquire even after several sessions, the authors write, and the causes of that inter-individual variability remain an open question. Each session comprises 2 eyes-open resting-state recordings of 3 minutes each plus 6 runs in which participants either sustained right-hand motor imagery or stayed at rest to control the position of a virtual cursor, and the release also includes anonymized MRI scans and behavioral scores; the authors say they hope the sample size and range of modalities will support analyses beyond brain network reorganization.

Hospices Civils de Lyon has registered a clinical study in France to decode motor imagery from non-invasive brain recordings as a prerequisite for innovative motor rehabilitation therapies. Combining MRI, MEG, and EEG, the study will design a subject-specific neurophysiological model, noting that standard BCI approaches neglect transient features such as beta bursts. The approach will first be validated in healthy subjects, then assessed for feasibility in stroke patients.

A preprint proposes interpretable metrics that separately quantify temporal, spatial, and frequency variability in BCI-related brain activity, tested across two motor-imagery BCI datasets totaling 133 participants and validated through within-subject and cross-subject classification experiments. The researchers report negative correlations of -0.2 to -0.4 under most conditions, indicating lower variability tracks higher BCI performance, and note the metrics show deep-learning and Riemannian classifiers differ in robustness to variability, with weaker correlations for the former. The work has not been peer reviewed.

Grenoble University Hospital has registered a clinical study (NCT07698496) assessing the feasibility and effectiveness of a chronic brain-computer interface for speech rehabilitation in people with locked-in syndrome (LIS), where non-invasive communication carries high cognitive load and existing invasive speech BCIs still rely on percutaneous connectors with infection risk. Using an intracranial epidural BCI paired with a speech synthesizer, the SpeechBCI protocol will test two complementary approaches in the same subject — a speech BCI (primary objective, the BCI_PAROLE device) and a cursor BCI (secondary), both running on the WIMAGINE intracranial epidural system. The trial is in the registration stage and has no clinical results yet.