MENTAL WORKLOAD CLASSIFICATION BASED ON VISUAL AND OPERATIONAL EEG SIGNALS

Hongquan Qu; Yuzhe Liu; Liping Pang; Yiping Shan

doi:10.1049/icp.2021.0170

MENTAL WORKLOAD CLASSIFICATION BASED ON VISUAL AND OPERATIONAL EEG SIGNALS

Hongquan Qu
, Yuzhe Liu
, Liping Pang^*
, Yiping Shan

^*Corresponding author for this work

School of Aeronautic Science and Engineering

North China University of Technology

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

1 Scopus citations

Abstract

The degree of mental workload directly affects the accuracy and safety of the task in the human-computer operating system, so it is very meaningful to study the state of mental workload of the operator. The common classification methods of mental workload direct uses EEG features to classify, which has low accuracy. This paper proposes a classification method with high accuracy and reliability for the mental workload classification of visual and operational task. This method directly extracts the energy characteristics of four different frequency bands from the independent components of the EEG, and then classifies them. The research results show that the accuracy of the proposed method is improved by 25.62%.

Original language	English
Title of host publication	IET Conference Proceedings
Publisher	Institution of Engineering and Technology
Pages	831-836
Number of pages	6
Volume	2020
Edition	3
ISBN (Electronic)	9781839534195
DOIs	https://doi.org/10.1049/icp.2021.0170
State	Published - 2020
Event	2020 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2020 - Virtual, Online Duration: 18 Sep 2020 → 21 Sep 2020

Conference

Conference	2020 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2020
City	Virtual, Online
Period	18/09/20 → 21/09/20

Keywords

EEG
INDEPENDENT COMPONENT ANALYSIS
MENTAL WORKLOAD CLASSIFICATION
SUPPORT VECTOR MACHINE

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10.1049/icp.2021.0170

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@inproceedings{153c8704ff4e491d98fee7230f9547ad,

title = "MENTAL WORKLOAD CLASSIFICATION BASED ON VISUAL AND OPERATIONAL EEG SIGNALS",

abstract = "The degree of mental workload directly affects the accuracy and safety of the task in the human-computer operating system, so it is very meaningful to study the state of mental workload of the operator. The common classification methods of mental workload direct uses EEG features to classify, which has low accuracy. This paper proposes a classification method with high accuracy and reliability for the mental workload classification of visual and operational task. This method directly extracts the energy characteristics of four different frequency bands from the independent components of the EEG, and then classifies them. The research results show that the accuracy of the proposed method is improved by 25.62\%.",

keywords = "EEG, INDEPENDENT COMPONENT ANALYSIS, MENTAL WORKLOAD CLASSIFICATION, SUPPORT VECTOR MACHINE",

author = "Hongquan Qu and Yuzhe Liu and Liping Pang and Yiping Shan",

note = "Publisher Copyright: {\textcopyright} 2020 The Institution of Engineering and Technology.; 2020 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2020 ; Conference date: 18-09-2020 Through 21-09-2020",

year = "2020",

doi = "10.1049/icp.2021.0170",

language = "英语",

volume = "2020",

pages = "831--836",

booktitle = "IET Conference Proceedings",

publisher = "Institution of Engineering and Technology",

address = "英国",

edition = "3",

}

TY - GEN

T1 - MENTAL WORKLOAD CLASSIFICATION BASED ON VISUAL AND OPERATIONAL EEG SIGNALS

AU - Qu, Hongquan

AU - Liu, Yuzhe

AU - Pang, Liping

AU - Shan, Yiping

PY - 2020

Y1 - 2020

N2 - The degree of mental workload directly affects the accuracy and safety of the task in the human-computer operating system, so it is very meaningful to study the state of mental workload of the operator. The common classification methods of mental workload direct uses EEG features to classify, which has low accuracy. This paper proposes a classification method with high accuracy and reliability for the mental workload classification of visual and operational task. This method directly extracts the energy characteristics of four different frequency bands from the independent components of the EEG, and then classifies them. The research results show that the accuracy of the proposed method is improved by 25.62%.

AB - The degree of mental workload directly affects the accuracy and safety of the task in the human-computer operating system, so it is very meaningful to study the state of mental workload of the operator. The common classification methods of mental workload direct uses EEG features to classify, which has low accuracy. This paper proposes a classification method with high accuracy and reliability for the mental workload classification of visual and operational task. This method directly extracts the energy characteristics of four different frequency bands from the independent components of the EEG, and then classifies them. The research results show that the accuracy of the proposed method is improved by 25.62%.

KW - EEG

KW - INDEPENDENT COMPONENT ANALYSIS

KW - MENTAL WORKLOAD CLASSIFICATION

KW - SUPPORT VECTOR MACHINE

UR - https://www.scopus.com/pages/publications/85171271962

U2 - 10.1049/icp.2021.0170

DO - 10.1049/icp.2021.0170

M3 - 会议稿件

AN - SCOPUS:85171271962

VL - 2020

SP - 831

EP - 836

BT - IET Conference Proceedings

PB - Institution of Engineering and Technology

T2 - 2020 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2020

Y2 - 18 September 2020 through 21 September 2020

ER -

MENTAL WORKLOAD CLASSIFICATION BASED ON VISUAL AND OPERATIONAL EEG SIGNALS

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