Deep Learning Based Inter-subject Continuous Decoding of Motor Imagery for Practical Brain-Computer Interfaces

Inter-subject transfer learning is a long-standing problem in brain-computer interfaces (BCIs) and has not yet been fully realized due to high inter-subject variability in the brain signals related to motor imagery (MI). The recent success of deep learning-based algorithms in classifying different b...

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Veröffentlicht in:	Frontiers in neuroscience Jg. 14; S. 918
Hauptverfasser:	Roy, Sujit, Chowdhury, Anirban, McCreadie, Karl, Prasad, Girijesh
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Lausanne Frontiers Research Foundation 30.09.2020 Frontiers Media S.A
Schlagworte:	Adaptation adaptive learning Algorithms Bayesian analysis brain-computer interface (BCI) Calibration Classification Competition convolutional neural network (CNN) Datasets Deep learning Discriminant analysis EEG electroencephalography (EEG) Feedback Interfaces Mental task performance motor imagery Neural networks Neuroscience Transfer learning
ISSN:	1662-453X, 1662-4548, 1662-453X
Online-Zugang:	Volltext
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