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Review Article | Open Access

A review of artificial intelligence for EEG-based brain−computer interfaces and applications

School of ICT, University of Tasmania, Hobart, TAS 7001, Australia
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Abstract

The advancement in neuroscience and computer science promotes the ability of the human brain to communicate and interact with the environment, making brain-computer interface (BCI) top interdisciplinary research. Furthermore, with the modern technology advancement in artificial intelligence (AI), including machine learning (ML) and deep learning (DL) methods, there is vast growing interest in the electroencephalogram (EEG)-based BCIs for AI-related visual, literal, and motion applications. In this review study, the literature on mainstreams of AI for the EEG-based BCI applications is investigated to fill gaps in the interdisciplinary BCI field. Specifically, the EEG signals and their main applications in BCI are first briefly introduced. Next, the latest AI technologies, including the ML and DL models, are presented to monitor and feedback human cognitive states. Finally, some BCI-inspired AI applications, including computer vision, natural language processing, and robotic control applications, are presented. The future research directions of the EEG-based BCI are highlighted in line with the AI technologies and applications.

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Brain Science Advances
Pages 162-170
Cite this article:
Cao Z. A review of artificial intelligence for EEG-based brain−computer interfaces and applications. Brain Science Advances, 2020, 6(3): 162-170. https://doi.org/10.26599/BSA.2020.9050017

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Received: 28 May 2020
Revised: 08 June 2020
Accepted: 12 June 2020
Published: 04 February 2021
© The authors 2020

This article is published with open access at journals.sagepub.com/home/BSA

Creative Commons Non Commercial CC BY- NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/ en-us/nam/open-access-at-sage).

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