A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface

Abstract Recent advancements in magnetoencephalography (MEG)-based brain-computer interfaces (BCIs) have shown great potential. However, the performance of current MEG-BCI systems is still inadequate and one main reasons for this unavailability open-source datasets. MEG are expensive hence datasets not readily available researchers to develop effective efficient BCI-related signal processing algorithms. In work, we release a 306-channel data recorded at 1KHz sampling frequency during four mental imagery tasks (i.e. hand imagery, feet subtraction word generation imagery). The dataset contains two sessions recordings performed on separate days from 17 healthy participants using typical BCI paradigm. will be only publicly as per our knowledge. can used by scientific community towards development novel pattern recognition machine learning methods detect brain activities related motor cognitive signals.