EEG Signal Processing-Driven Machine Learning for Cognitive Task Recognition

Siran Wang; Brian Lee; Gary Tse; Haipeng Liu

doi:10.1201/9781003479970-11

EEG Signal Processing-Driven Machine Learning for Cognitive Task Recognition

Siran Wang
, Brian Lee
, Gary Tse
, Haipeng Liu

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

2 Citations (Scopus)

126 Downloads (Pure)

Abstract

Cognitive task recognition plays a key role in human–computer interaction with increasing research interest in recent years. One of the most practical approaches to achieving this goal is through the use of electroencephalogram (EEG), which provides an objective estimation of human cognitive states. However, accurate recognition remains challenging since EEG is characterized by individual difference, nonstationarity, and sensitivity to artifacts. To tackle this issue, artificial intelligence (AI) techniques have been employed to improve the efficacy of EEG signal processing and feature extraction, as well as classification through revealing more salient features and discriminant information. Moreover, a significant body of research has provided more complete and comprehensive datasets for this technique, providing a wider range of applications and greater accessibility. By enabling precise diagnoses and personalized treatment, the AI-based methods provide the potential to enhance the quality of life for the patients of various psychological and neurological diseases.

Original language	English
Title of host publication	Artificial Intelligence Enabled Signal Processing based Models for Neural Information Processing
Editors	Rajesh Kumar Tripathy, Ram Bilas Pachori
Place of Publication	Boca Raton
Publisher	CRC Press, Taylor & Francis Group
Chapter	11
Pages	168-187
Number of pages	20
Edition	1
ISBN (Electronic)	9781003479970
ISBN (Print)	9781032529301
DOIs	https://doi.org/10.1201/9781003479970-11
Publication status	Published - 6 Jun 2024

Bibliographical note

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Cognitive task recognition
Neuroscience
EEG

Access to Document

10.1201/9781003479970-11Licence: Unspecified

Liu2024AAMAccepted author manuscript, 945 KBLicence: CC BY-NC-ND

Cite this

EEG Signal Processing-Driven Machine Learning for Cognitive Task Recognition. / Wang, Siran; Lee, Brian; Tse, Gary et al.
Artificial Intelligence Enabled Signal Processing based Models for Neural Information Processing. ed. / Rajesh Kumar Tripathy; Ram Bilas Pachori. 1. ed. Boca Raton: CRC Press, Taylor & Francis Group, 2024. p. 168-187.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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abstract = "Cognitive task recognition plays a key role in human–computer interaction with increasing research interest in recent years. One of the most practical approaches to achieving this goal is through the use of electroencephalogram (EEG), which provides an objective estimation of human cognitive states. However, accurate recognition remains challenging since EEG is characterized by individual difference, nonstationarity, and sensitivity to artifacts. To tackle this issue, artificial intelligence (AI) techniques have been employed to improve the efficacy of EEG signal processing and feature extraction, as well as classification through revealing more salient features and discriminant information. Moreover, a significant body of research has provided more complete and comprehensive datasets for this technique, providing a wider range of applications and greater accessibility. By enabling precise diagnoses and personalized treatment, the AI-based methods provide the potential to enhance the quality of life for the patients of various psychological and neurological diseases.",

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N1 - Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

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AB - Cognitive task recognition plays a key role in human–computer interaction with increasing research interest in recent years. One of the most practical approaches to achieving this goal is through the use of electroencephalogram (EEG), which provides an objective estimation of human cognitive states. However, accurate recognition remains challenging since EEG is characterized by individual difference, nonstationarity, and sensitivity to artifacts. To tackle this issue, artificial intelligence (AI) techniques have been employed to improve the efficacy of EEG signal processing and feature extraction, as well as classification through revealing more salient features and discriminant information. Moreover, a significant body of research has provided more complete and comprehensive datasets for this technique, providing a wider range of applications and greater accessibility. By enabling precise diagnoses and personalized treatment, the AI-based methods provide the potential to enhance the quality of life for the patients of various psychological and neurological diseases.

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KW - Neuroscience

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ER -

EEG Signal Processing-Driven Machine Learning for Cognitive Task Recognition

Abstract

Bibliographical note

UN SDGs

Keywords

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