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

被引:1
作者
Shahbakhti, Mohammad [1 ]
Beiramvand, Matin [2 ]
Nasiri, Erfan [3 ]
Chen, Wei [4 ,5 ,6 ]
Sole-Casals, Jordi [7 ]
Wierzchon, Michal [8 ]
Broniec-Wojcik, Anna [9 ]
Augustyniak, Piotr [9 ]
Marozas, Vaidotas [1 ]
机构
[1] Kaunas Univ Technol, Biomed Engn Inst, Kaunas, Lithuania
[2] Tampere Univ, Fac Informat Technol & Commun, Tampere, Finland
[3] Allameh Tabatabai Univ, Fac Stat Math & Comp, Tehran, Iran
[4] Fudan Univ, Ctr Intelligent Med Elect, Shanghai, Peoples R China
[5] Fudan Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China
[6] Fudan Univ, Human Phenome Inst, Shanghai, Peoples R China
[7] Univ Vic, Data & Signal Proc Res Grp, Cent Univ Catalonia, Vic, Spain
[8] Jagiellonian Univ, Inst Psychol, Krakow, Poland
[9] AGH Univ Sci & Technol, Dept Biocybernet & Biomed Engn, Krakow, Poland
来源
2022 14TH BIOMEDICAL ENGINEERING INTERNATIONAL CONFERENCE (BMEICON 2022) | 2022年
关键词
EEG; Fatigue; Driving; Gender;
D O I
10.1109/BMEiCON56653.2022.10012118
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
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.
引用
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页数:3
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