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

Dheeraj Rathee, Haider Raza, Sujit Roy, Girijesh Prasad

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
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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 of the main reasons for this is the
unavailability of open-source MEG-BCI datasets. MEG systems are expensive and hence MEG datasets are not readily available for researchers to develop effective and efficient BCI-related signal processing algorithms. In this work, we release a
306-channel MEG-BCI data recorded at 1KHz sampling frequency during four mental imagery tasks (i.e. hand imagery, feet imagery, subtraction imagery, and word generation imagery). The dataset contains two sessions of MEG recordings performed on separate days from 17 healthy participants using a typical BCI imagery paradigm. The current dataset will be the only publicly available MEG imagery BCI dataset as per our knowledge. The dataset can be used by the scientific community towards the development of novel pattern recognition machine learning methods to detect brain activities related to motor
imagery and cognitive imagery tasks using MEG signals.
Original languageEnglish
Article number120
Number of pages13
JournalScientific Data
Issue number1
Early online date29 Apr 2021
Publication statusPublished (in print/issue) - Dec 2021

Bibliographical note

Publisher Copyright:
© 2021, The Author(s).


  • MEG
  • BCI
  • motor and cognitive imagery data
  • Brain-Computer Interfaces
  • Neuroimaging
  • Magnetoencephalography
  • Humans
  • Male
  • Cognition
  • Motor Activity
  • Machine Learning
  • Young Adult
  • Adult
  • Female
  • Pattern Recognition, Automated


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