Improving Generalization of CNN-Based Motor-Imagery EEG Decoders via Dynamic Convolutions

Improving Generalization of CNN-Based Motor-Imagery EEG Decoders via Dynamic Convolutions

Deep Convolutional Neural Networks (CNNs) have recently demonstrated impressive results in electroencephalogram (EEG) decoding for several Brain-Computer Interface (BCI) paradigms, including Motor-Imagery (MI). However, neurophysiological processes underpinning EEG signals vary across subjects causi...

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Bibliographic Details
Main Authors: Konstantinos Barmpas, Yannis Panagakis, Stylianos Bakas, Dimitrios A. Adamos, Nikolaos Laskaris, Stefanos Zafeiriou
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
Brain-computer interfaces (BCIs)
causality
motor-imagery (MI)
electroencephalogram (EEG)
Online Access:https://ieeexplore.ieee.org/document/10093935/

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https://ieeexplore.ieee.org/document/10093935/

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