ArticleScientific reports2018
Across-subject offline decoding of motor imagery from MEG and EEG.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Hierarchical Organization of Human Visual Feature Attention Control.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- 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
- Revisiting the role of computational neuroimaging in the era of integrative neuroscience.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- High-density transparent graphene arrays for predicting cellular calcium activity at depth from surface potential recordings.Nature nanotechnology · 2024Article
- Group-level brain decoding with deep learning.Human brain mapping · 2023Article
- The effect of visual and proprioceptive feedback on sensorimotor rhythms during BCI training.PloS one · 2022Article
- Most favorable stimulation duration in the sensorimotor cortex for fNIRS-based BCI.Biomedical optics express · 2021Article
- Immune gene prognostic signature for disease free survival of gastric cancer: Translational research of an artificial intelligence survival predictive system.Computational and structural biotechnology journal · 2021Article
- Bioinformatics Identified 17 Immune Genes as Prognostic Biomarkers for Breast Cancer: Application Study Based on Artificial Intelligence Algorithms.Frontiers in oncology · 2020Article
- Inter- and Intra-individual Variability in Brain Oscillations During Sports Motor Imagery.Frontiers in human neuroscience · 2020Article
- Deep Learning Based Inter-subject Continuous Decoding of Motor Imagery for Practical Brain-Computer Interfaces.Frontiers in neuroscience · 2020Article
- Adaptive neural network classifier for decoding MEG signals.NeuroImage · 2019Article
- Visual and kinesthetic modes affect motor imagery classification in untrained subjects.Scientific reports · 2019Article
- Advances in Hybrid Brain-Computer Interfaces: Principles, Design, and Applications.Computational intelligence and neuroscience · 2019Review
- Single-Trial Decoding of Scalp EEG under Natural Conditions.Computational intelligence and neuroscience · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Long calibration time hinders the feasibility of brain-computer interfaces (BCI). If other subjects' data were used for training the classifier, BCI-based neurofeedback practice could start without the initial calibration. Here, we compare methods for inter-subject decoding of left- vs. right-hand motor imagery (MI) from MEG and EEG. Six methods were tested on data involving MEG and EEG measurements of healthy participants. Inter-subject decoders were trained on subjects showing good within-subject accuracy, and tested on all subjects, including poor performers. Three methods were based on Common Spatial Patterns (CSP), and three others on logistic regression with l
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