Source-space brain functional connectivity features in electroencephalogram-based driver fatigue classification

Khanh Ha Nguyen; Matthew Ebbatson; Yvonne Tran; Ashley Craig; Hung Nguyen; Rifai Chai

doi:10.3390/s23052383

Source-space brain functional connectivity features in electroencephalogram-based driver fatigue classification

Khanh Ha Nguyen, Matthew Ebbatson, Yvonne Tran, Ashley Craig, Hung Nguyen, Rifai Chai^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

102 Downloads (Pure)

Abstract

This study examined the brain source space functional connectivity from the electroencephalogram (EEG) activity of 48 participants during a driving simulation experiment where they drove until fatigue developed. Source-space functional connectivity (FC) analysis is a state-of-the-art method for understanding connections between brain regions that may indicate psychological differences. Multi-band FC in the brain source space was constructed using the phased lag index (PLI) method and used as features to train an SVM classification model to classify driver fatigue and alert conditions. With a subset of critical connections in the beta band, a classification accuracy of 93% was achieved. Additionally, the source-space FC feature extractor demonstrated superiority over other methods, such as PSD and sensor-space FC, in classifying fatigue. The results suggested that source-space FC is a discriminative biomarker for detecting driving fatigue.

Original language	English
Article number	2383
Pages (from-to)	1-18
Number of pages	18
Journal	Sensors
Volume	23
Issue number	5
DOIs	https://doi.org/10.3390/s23052383
Publication status	Published - 1 Mar 2023

Bibliographical note

Copyright the Author(s) 2023. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

driver fatigue
driving fatigue classification
electroencephalogram
EEG
source space functional connectivity

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10.3390/s23052383Licence: CC BY

Publisher version (open access)Final published version, 1.92 MBLicence: CC BY

Cite this

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title = "Source-space brain functional connectivity features in electroencephalogram-based driver fatigue classification",

abstract = "This study examined the brain source space functional connectivity from the electroencephalogram (EEG) activity of 48 participants during a driving simulation experiment where they drove until fatigue developed. Source-space functional connectivity (FC) analysis is a state-of-the-art method for understanding connections between brain regions that may indicate psychological differences. Multi-band FC in the brain source space was constructed using the phased lag index (PLI) method and used as features to train an SVM classification model to classify driver fatigue and alert conditions. With a subset of critical connections in the beta band, a classification accuracy of 93\% was achieved. Additionally, the source-space FC feature extractor demonstrated superiority over other methods, such as PSD and sensor-space FC, in classifying fatigue. The results suggested that source-space FC is a discriminative biomarker for detecting driving fatigue.",

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AU - Nguyen, Khanh Ha

AU - Ebbatson, Matthew

AU - Tran, Yvonne

AU - Craig, Ashley

AU - Nguyen, Hung

AU - Chai, Rifai

N1 - Copyright the Author(s) 2023. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2023/3/1

Y1 - 2023/3/1

N2 - This study examined the brain source space functional connectivity from the electroencephalogram (EEG) activity of 48 participants during a driving simulation experiment where they drove until fatigue developed. Source-space functional connectivity (FC) analysis is a state-of-the-art method for understanding connections between brain regions that may indicate psychological differences. Multi-band FC in the brain source space was constructed using the phased lag index (PLI) method and used as features to train an SVM classification model to classify driver fatigue and alert conditions. With a subset of critical connections in the beta band, a classification accuracy of 93% was achieved. Additionally, the source-space FC feature extractor demonstrated superiority over other methods, such as PSD and sensor-space FC, in classifying fatigue. The results suggested that source-space FC is a discriminative biomarker for detecting driving fatigue.

AB - This study examined the brain source space functional connectivity from the electroencephalogram (EEG) activity of 48 participants during a driving simulation experiment where they drove until fatigue developed. Source-space functional connectivity (FC) analysis is a state-of-the-art method for understanding connections between brain regions that may indicate psychological differences. Multi-band FC in the brain source space was constructed using the phased lag index (PLI) method and used as features to train an SVM classification model to classify driver fatigue and alert conditions. With a subset of critical connections in the beta band, a classification accuracy of 93% was achieved. Additionally, the source-space FC feature extractor demonstrated superiority over other methods, such as PSD and sensor-space FC, in classifying fatigue. The results suggested that source-space FC is a discriminative biomarker for detecting driving fatigue.

KW - driver fatigue

KW - driving fatigue classification

KW - electroencephalogram

KW - EEG

KW - source space functional connectivity

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JO - Sensors

JF - Sensors

IS - 5

M1 - 2383

ER -

Source-space brain functional connectivity features in electroencephalogram-based driver fatigue classification

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Keywords

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