ArticleNeuroImage2019
Adaptive neural network classifier for decoding MEG signals.
Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Electroencephalography, Magnetoencephalography, and Cognitive Reserve: A Systematic Review.Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists · 2021Pooled it
- BrainDEC: An M/EEG Unsupervised Representation Learning Framework with Disentangled Equivariance Constraint.Interdisciplinary sciences, computational life sciences · 2026Article
- Enhancing fMRI Decoded Neurofeedback with Co-adaptive Training: Simulation and Proof-of-principle Evidence.Neuroinformatics · 2026Article
- Anatomically Veridical On-Scalp Sensor Topographies.The European journal of neuroscience · 2025Article
- Decoding in the Fourth Dimension: Classification of Temporal Patterns and Their Generalization Across Locations.Human brain mapping · 2025Article
- Convolutional neural networks decode finger movements in motor sequence learning from MEG data.Frontiers in neuroscience · 2025Article
- Coherence-based channel selection and Riemannian geometry features for magnetoencephalography decoding.Cognitive neurodynamics · 2024Article
- Improving source estimation of retinotopic MEG responses by combining data from multiple subjects.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Robust discrimination of multiple naturalistic same-hand movements from MEG signals with convolutional neural networks.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Group-level brain decoding with deep learning.Human brain mapping · 2023Article
- Machine learning and deep learning approach for medical image analysis: diagnosis to detection.Multimedia tools and applications · 2022Article
- The relationship between frequency content and representational dynamics in the decoding of neurophysiological data.NeuroImage · 2022Article
- Categorizing objects from MEG signals using EEGNet.Cognitive neurodynamics · 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
- Comparing Methods of Feature Extraction of Brain Activities for Octave Illusion Classification Using Machine Learning.Sensors (Basel, Switzerland) · 2021Article
- MEG-Based Detection of Voluntary Eye Fixations Used to Control a Computer.Frontiers in neuroscience · 2021Article
- Deep Learning Based Inter-subject Continuous Decoding of Motor Imagery for Practical Brain-Computer Interfaces.Frontiers in neuroscience · 2020Article
- Decoding Movement From Electrocorticographic Activity: A Review.Frontiers in neuroinformatics · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We introduce two Convolutional Neural Network (CNN) classifiers optimized for inferring brain states from magnetoencephalographic (MEG) measurements. Network design follows a generative model of the electromagnetic (EEG and MEG) brain signals allowing explorative analysis of neural sources informing classification. The proposed networks outperform traditional classifiers as well as more complex neural networks when decoding evoked and induced responses to different stimuli across subjects. Importantly, these models can successfully generalize to new subjects in real-time classification enabling more efficient brain-computer interfaces (BCI).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.