EEG-Based Hypo-vigilance Detection Using Convolutional Neural Network

Boudaya, Amal; Bouaziz, Bassem; Chaabene, Siwar; Chaâri, Lotfi; Ammar, Achraf; Hökelmann, Anita

doi:10.1007/978-3-030-51517-1_6

Cited by 11 publications

(5 citation statements)

References 20 publications

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“…This state is mainly defined by heaviness in terms of reaction, changes in behavior, reflex reduction, and the difficulty of keeping the head in the frontal position of the vision field. In this regard, several means such as videos [ 7 , 10 ] and biomedical signals [ 11 , 12 ] have been targeted for DD. On the one side, the video-based applications for DD are efficient and robust against noise and lighting variations [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures; (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works.

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Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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“…Beta waves are associated with alertness and normal waking consciousness [42], [43]. The Beta range of human EEG signals is highly connected with the analysis of different cognitive processes like recognition tasks and informational differentiation processes [44].…”

Section: Discussionmentioning

confidence: 99%

Brain Region-Based Vigilance Assessment Using Electroencephalography and Eye Tracking Data Fusion

et al. 2022

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Vigilance is the capacity to remain alert for an extended time while performing a task. Staying alert is obligatory in many jobs, particularly those that involve monitoring, such as surveillance tasks, security monitoring, and air traffic control. These monitoring tasks require a specific level of arousal to maintain an adequate level of cognitive efficiency. In this study, we investigate the possibility of assessing the vigilance levels using a fusion of electroencephalography (EEG) and eye tracking data. Vigilance levels were established by performing a modified version of the Stroop color word task (SCWT) for 30 minutes. Feature-level fusion based on the canonical correlation analysis (CCA) was employed to each brain region to improve the classification accuracy of vigilance level assessment. Results obtained using support vector machines (SVM) classifier show that fusion of EEG+eye tracking modalities has improved the classification accuracy compared to individual modality. The EEG+Eye tracking fusion on the right central brain region achieved the highest classification accuracy of 97.4 ± 1.3%, compared to the individual Beta EEG with 92.0± 7.3% and Eye tracking with 76.8± 8.4%, respectively. Likewise, EEG and Eye tracking fusion on the right frontal region showed classification accuracy of 96.9 ± 1.1% for both the Alpha and Beta bands. Meanwhile, when all brain regions were utilized, the highest classification accuracy of EEG+Eye tracking was 96.8 ± 0.6% using Delta band compared to the EEG alone with 88.18 ± 8.5% and eye tracking alone with 76.8 ± 8.4 %, respectively. The overall results showed that vigilance is a brain region specific and the fusion of EEG+ and Eye tracking data using CCA has significantly improved the classification accuracy of vigilance levels assessment.

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“…Ko et al [12] proposed a Convolutional Neural Network (CNN) based model that classifies driver vigilance states categorized into three classes, i.e., awake, tired, and drowsy. Boudaya et al [13] also proposed Fig. 1.…”

Section: Introductionmentioning

confidence: 98%

“…EEG-based driver fatigue state analysis: Exiting works for driver fatigue state analysis are mostly depend on traditional machine learning methods with hand-crafted features. In recent, Deep Neural Network (DNN) based EEG-based driver fatigue detection models have been proposed [12,13] with a great success of deep learning. Ko et al [12] proposed a Convolutional Neural Network (CNN) based model that classifies driver vigilance states categorized into three classes, i.e., awake, tired, and drowsy.…”

Section: Introductionmentioning

confidence: 99%

Mitigating Inter-Subject Brain Signal Variability FOR EEG-Based Driver Fatigue State Classification

Hwang

Park

Kim

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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With great research advances on Brain-Computer-Interface (BCI) systems, Electroencephalography (EEG) based driver fatigue state classification models have shown its effectiveness. However, EEG signals contain large differences between individuals, making it hard to build a unified model among individuals. In this paper, we propose a subjectindependent EEG-based driver fatigue state (i.e., awake, tired, and drowsy) classification model that mitigates a performance gap between subjects. To this end, we exploit an adversarial training strategy to make our classification model misclassify the subject labels. Besides, we propose an Intersubject Feature Distance Minimization (IFDM) method that minimizes the Wasserstein distance between two different subject groups of the same class to reduce the individual performance discrepancy. Our method is also designed to enable training even if the subject labels are not sufficiently included in the EEG dataset. To demonstrate the ability of the proposed method, we conduct a drowsiness classification task on a publicly available SEED-VIG dataset. The experimental results show our model achieves the highest accuracy and the lowest individual performance variability.

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EEG-Based Hypo-vigilance Detection Using Convolutional Neural Network

Cited by 11 publications

References 20 publications

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Brain Region-Based Vigilance Assessment Using Electroencephalography and Eye Tracking Data Fusion

Mitigating Inter-Subject Brain Signal Variability FOR EEG-Based Driver Fatigue State Classification

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