This mini-review investigates the performance improvement of noninvasive brain-computer interfaces (BCIs) for communication and motor control in clinical applications. It focuses on studies published between 2011 and 2021, analyzing trends in BCI research, challenges, limitations, and potential solutions, particularly concerning data augmentation techniques using deep learning models to address data scarcity issues. The review also proposes future research directions, including the exploration of the Global Workspace Theory for enhancing BCI versatility.
Publisher
Journal of Medical and Biological Engineering
Published On
Jan 01, 2024
Authors
Yuya Saito, Koji Kamagata, Toshiaki Akashi, Akihiko Wada, Keigo Shimoji, Masaaki Hori, Masaru Kuwabara, Ryota Kanai, Shigeki Aoki
Tags
brain-computer interfaces
communication
motor control
data augmentation
deep learning
clinical applications
Global Workspace Theory
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