ArticleScientific data2021
A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface.
Article in Scientific data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Brain-Computer Interfaces: The Dawn of a New Era in Disease Treatment.Exploration (Beijing, China) · 2026Review
- Recognition of brain activities via graph-based long short-term memory-convolutional neural network.Frontiers in neuroscience · 2025Article
- Coherence-based channel selection and Riemannian geometry features for magnetoencephalography decoding.Cognitive neurodynamics · 2024Article
- A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces.Scientific data · 2023Article
- Mapping and decoding cortical engagement during motor imagery, mental arithmetic, and silent word generation using MEG.Human brain mapping · 2023Article
- Brain-computer interface paradigms and neural coding.Frontiers in neuroscience · 2023Review
- Applying antagonistic activation pattern to the single-trial classification of mental arithmetic.Heliyon · 2022Article
- [Key technologies for intelligent brain-computer interaction based on magnetoencephalography].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- Explainable artificial intelligence model to predict brain states from fNIRS signals.Frontiers in human neuroscience · 2022Article
- A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface.Scientific data · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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Registered trials
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