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 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.
Highlights
Background & SummaryMental imagery activities such as imagination of limb movement or mathematical calculation induce explicit and predictive patterns of brain activity that can be detected using electroencephalography (EEG) or magnetoencephalography (MEG)1
MI-based Brain-computer interfaces (BCIs) employed with neurofeedback training paradigms can induce brain plasticity and possibly contribute to the enhancement of motor rehabilitation for stoke patients5–7, may provide an alternative to conventional recovery methods e.g. physical practice8 for these patients
While majority of the research to date has focused on EEG modality, MEG can be useful towards developing effective BCI systems9,10
Summary
Mental imagery activities such as imagination of limb movement or mathematical calculation induce explicit and predictive patterns of brain activity that can be detected using electroencephalography (EEG) or magnetoencephalography (MEG). Brain-computer interfaces (BCIs) can detect and translate these patterns into actions and provide a potential medium for communication and rehabilitation for patients with severe neuromuscular impairment. MI-based BCIs employed with neurofeedback training paradigms can induce brain plasticity and possibly contribute to the enhancement of motor rehabilitation for stoke patients, may provide an alternative to conventional recovery methods e.g. physical practice for these patients. Compared to EEG, MEG allows detection of higher frequencies as magnetic fields are less attenuated by the head bone and tissue as compared to electric fields. 66 minutes of MEG recordings and 400 imagery trials are available per participant. The dataset is one of the first MI- and CI-related MEG-based BCI datasets published to date and presents a significant step from existing datasets in terms of uniformity, state-of-the-art MEG system, number of participants and MEG channels
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