Improving cross-subject classification performance of motor imagery signals: a data augmentation-focused deep learning framework

Improving cross-subject classification performance of motor imagery signals: a data augmentation-focused deep learning framework

Motor imagery brain-computer interfaces (MI-BCIs) have gained a lot of attention in recent years thanks to their potential to enhance rehabilitation and control of prosthetic devices for individuals with motor disabilities. However, accurate classification of motor imagery signals remains a challeng...

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Bibliographic Details
Main Authors: Enes Ozelbas, Emine Elif Tülay, Serhat Ozekes
Format: Article
Language:English
Published: IOP Publishing 2024-01-01
Series:Machine Learning: Science and Technology
Subjects:
brain computer interface
data augmentation
deep learning
electroencephalogram
human computer interaction
motor imagery
Online Access:https://doi.org/10.1088/2632-2153/ad200c

Internet

https://doi.org/10.1088/2632-2153/ad200c

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