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

Zehong Cao

doi:10.26599/BSA.2020.9050017

Brain Science Advances 2020, 6(3): 162-170 https://doi.org/10.26599/BSA.2020.9050017

Review Article |

Open Access | Issue | Published: 04 February 2021

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

Show Author's Information Hide Author's Information Zehong Cao(

)

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

Keywords:

electroencephalogram (EEG), brain-computer interface (BCI), artificial intelligence, computer vision, natural language processing, robot controls

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

Show Author's information Hide Author's Information Zehong Cao(

)

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

Abstract

Keywords: electroencephalogram (EEG), brain-computer interface (BCI), artificial intelligence, computer vision, natural language processing, robot controls

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Publication history

Received: 28 May 2020

Revised: 08 June 2020

Accepted: 12 June 2020

Published: 04 February 2021

Issue date: September 2020

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

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