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Review of Performance Improvement of a Noninvasive Brain-computer Interface in Communication and Motor Control for Clinical Applications

Engineering and Technology

Review of Performance Improvement of a Noninvasive Brain-computer Interface in Communication and Motor Control for Clinical Applications

Y. Saito, K. Kamagata, et al.

Discover groundbreaking insights into noninvasive brain-computer interfaces (BCIs) for communication and motor control in clinical applications. This research, conducted by Yuya Saito, Koji Kamagata, Toshiaki Akashi, Akihiko Wada, Keigo Shimoji, Masaaki Hori, Masaru Kuwabara, Ryota Kanai, and Shigeki Aoki, delves into trends, challenges, and innovative data augmentation techniques using deep learning to enhance BCI versatility.

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~3 min • Beginner • English
Abstract
Herein, we focus on BCI studies published between 2011 and 2021 to investigate the most recent trends in BCI research in the area of communication and control for clinical applications, such as the extraction of action intentions and translation into electrical commands and the monitoring of human physiology in patients with motor disabilities. Further, this study discusses the challenges and limitations of BCI systems along with solutions to these issues. This study also suggests future research for introducing BCI systems to medical fields.
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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