The Importance of Gender Specification for Detection of Driver Fatigue using a Single EEG Channel

Mohammad Shahbakhti; Matin Beiramvand; Erfan Nasiri; Wei Chen; Jordi Solé-Casals; Michal Wierzchon; Anna Broniec-Wójcik; Piotr Augustyniak; Vaidotas Marozas

doi:10.1109/BMEiCON56653.2022.10012118

The Importance of Gender Specification for Detection of Driver Fatigue using a Single EEG Channel

Mohammad Shahbakhti, Matin Beiramvand, Erfan Nasiri, Wei Chen, Jordi Solé-Casals, Michal Wierzchon, Anna Broniec-Wójcik, Piotr Augustyniak, Vaidotas Marozas

Computing Sciences

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

2 Citations (Scopus)

Abstract

Although detection of the driver fatigue using a single electroencephalography (EEG) channel has been addressed in literature, the gender differentiation for applicability of the model has not been investigated heretofore. Motivated accordingly, we address the detection of driver fatigue based the gender-segregated datasets, where each of them contains 8 subjects. After splitting the EEG signal into its sub-bands (delta, theta, alpha, beta, and gamma) using discrete wavelet transform, the log energy entropy of each band is computed to form the feature vector. Afterwards, the feature vector is randomly split into 50% for training and 50% for the unseen testing, and fed to a support vector machine model. When comparing the classification results of fatigue driving detection between the gender segregated and non-gender segregated datasets, the former achieved the accuracy 78% and 77% for male and female subjects, respectively, than the latter (71%). The obtained results show the importance of gender-specification for the driver fatigue detection.

Original language	English
Title of host publication	BMEiCON 2022 - 14th Biomedical Engineering International Conference
Publisher	IEEE
Number of pages	3
ISBN (Electronic)	978-1-6654-8903-4
DOIs	https://doi.org/10.1109/BMEiCON56653.2022.10012118
Publication status	Published - 2023
Publication type	A4 Article in conference proceedings
Event	Biomedical Engineering International Conference - Songkhla, Thailand Duration: 10 Nov 2022 → 13 Nov 2022

Conference

Conference	Biomedical Engineering International Conference
Country/Territory	Thailand
City	Songkhla
Period	10/11/22 → 13/11/22

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10.1109/BMEiCON56653.2022.10012118

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Shahbakhti, M., Beiramvand, M., Nasiri, E., Chen, W., Solé-Casals, J., Wierzchon, M., Broniec-Wójcik, A., Augustyniak, P., & Marozas, V. (2023). The Importance of Gender Specification for Detection of Driver Fatigue using a Single EEG Channel. In BMEiCON 2022 - 14th Biomedical Engineering International Conference IEEE. https://doi.org/10.1109/BMEiCON56653.2022.10012118

@inproceedings{7bcff950356a46248c83b65514432307,

title = "The Importance of Gender Specification for Detection of Driver Fatigue using a Single EEG Channel",

abstract = "Although detection of the driver fatigue using a single electroencephalography (EEG) channel has been addressed in literature, the gender differentiation for applicability of the model has not been investigated heretofore. Motivated accordingly, we address the detection of driver fatigue based the gender-segregated datasets, where each of them contains 8 subjects. After splitting the EEG signal into its sub-bands (delta, theta, alpha, beta, and gamma) using discrete wavelet transform, the log energy entropy of each band is computed to form the feature vector. Afterwards, the feature vector is randomly split into 50% for training and 50% for the unseen testing, and fed to a support vector machine model. When comparing the classification results of fatigue driving detection between the gender segregated and non-gender segregated datasets, the former achieved the accuracy 78% and 77% for male and female subjects, respectively, than the latter (71%). The obtained results show the importance of gender-specification for the driver fatigue detection.",

author = "Mohammad Shahbakhti and Matin Beiramvand and Erfan Nasiri and Wei Chen and Jordi Sol{\'e}-Casals and Michal Wierzchon and Anna Broniec-W{\'o}jcik and Piotr Augustyniak and Vaidotas Marozas",

year = "2023",

doi = "10.1109/BMEiCON56653.2022.10012118",

language = "English",

booktitle = "BMEiCON 2022 - 14th Biomedical Engineering International Conference",

publisher = "IEEE",

address = "United States",

note = "Biomedical Engineering International Conference ; Conference date: 10-11-2022 Through 13-11-2022",

}

Shahbakhti, M, Beiramvand, M, Nasiri, E, Chen, W, Solé-Casals, J, Wierzchon, M, Broniec-Wójcik, A, Augustyniak, P & Marozas, V 2023, The Importance of Gender Specification for Detection of Driver Fatigue using a Single EEG Channel. in BMEiCON 2022 - 14th Biomedical Engineering International Conference. IEEE, Biomedical Engineering International Conference, Songkhla, Thailand, 10/11/22. https://doi.org/10.1109/BMEiCON56653.2022.10012118

TY - GEN

T1 - The Importance of Gender Specification for Detection of Driver Fatigue using a Single EEG Channel

AU - Shahbakhti, Mohammad

AU - Beiramvand, Matin

AU - Nasiri, Erfan

AU - Chen, Wei

AU - Solé-Casals, Jordi

AU - Wierzchon, Michal

AU - Broniec-Wójcik, Anna

AU - Augustyniak, Piotr

AU - Marozas, Vaidotas

PY - 2023

Y1 - 2023

N2 - Although detection of the driver fatigue using a single electroencephalography (EEG) channel has been addressed in literature, the gender differentiation for applicability of the model has not been investigated heretofore. Motivated accordingly, we address the detection of driver fatigue based the gender-segregated datasets, where each of them contains 8 subjects. After splitting the EEG signal into its sub-bands (delta, theta, alpha, beta, and gamma) using discrete wavelet transform, the log energy entropy of each band is computed to form the feature vector. Afterwards, the feature vector is randomly split into 50% for training and 50% for the unseen testing, and fed to a support vector machine model. When comparing the classification results of fatigue driving detection between the gender segregated and non-gender segregated datasets, the former achieved the accuracy 78% and 77% for male and female subjects, respectively, than the latter (71%). The obtained results show the importance of gender-specification for the driver fatigue detection.

AB - Although detection of the driver fatigue using a single electroencephalography (EEG) channel has been addressed in literature, the gender differentiation for applicability of the model has not been investigated heretofore. Motivated accordingly, we address the detection of driver fatigue based the gender-segregated datasets, where each of them contains 8 subjects. After splitting the EEG signal into its sub-bands (delta, theta, alpha, beta, and gamma) using discrete wavelet transform, the log energy entropy of each band is computed to form the feature vector. Afterwards, the feature vector is randomly split into 50% for training and 50% for the unseen testing, and fed to a support vector machine model. When comparing the classification results of fatigue driving detection between the gender segregated and non-gender segregated datasets, the former achieved the accuracy 78% and 77% for male and female subjects, respectively, than the latter (71%). The obtained results show the importance of gender-specification for the driver fatigue detection.

U2 - 10.1109/BMEiCON56653.2022.10012118

DO - 10.1109/BMEiCON56653.2022.10012118

M3 - Conference contribution

BT - BMEiCON 2022 - 14th Biomedical Engineering International Conference

PB - IEEE

T2 - Biomedical Engineering International Conference

Y2 - 10 November 2022 through 13 November 2022

ER -

The Importance of Gender Specification for Detection of Driver Fatigue using a Single EEG Channel

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