NETBCI Dataset Pairs MEG and EEG from 19 Users across Four BCI Training Sessions
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.
