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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Playback language: English

Index

Abstract

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

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