Understanding Brain-Computer Interfaces training: a longitudinal and multimodal dataset

Brain-Computer Interfaces (BCIs) are devices that translate brain activity into commands for control or communication that present many clinical applications. However, the ability to control a BCI remains a learned skill that a non-negligible proportion cannot acquire after several sessions. Identifying the causes of such inter-variability is still an open avenue. We share the Networks for BCI (NETBCI) dataset to help identifying brain networks reorganization underlying BCI training and to develop improved BCI systems. The NETBCI dataset contains magnetoencephalographic (MEG) and electroencephalographic (EEG) recordings obtained from 19 healthy subjects across four sessions performed on four different days. Each session comprises two resting-state recordings of three minutes each (eyes open), and six runs of BCI experiment consisting of performing either a sustained right hand motor imagery or of remaining at rest to control the position of a virtual cursor. NETBCI comprises also anonymized MRI and behavioral scores. We hope that NETBCI can be used for further analysis beyond the scope of the investigation of brain network reorganization during BCI training. We believe that the sample size and the number of modalities should be useful for the scientific community.