A robust and efficient EEG-based drowsiness detection system using different machine learning algorithms

Fouad, Islam A.

doi:10.1016/j.asej.2022.101895

Cited by 27 publications

(10 citation statements)

References 51 publications

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“…However, one of the weak points of this research is that it is sufficient to confirm fatigue based on the self-report questionnaire of the fatigue severity scale (FSS). Fouad et al [ 13 ] presented a new model to monitor two levels of driver fatigue. These researchers used the EEG signals of 12 subjects in their study.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Automatic Detection of Driver Fatigue Using EEG Signals Based on Graph Convolutional Networks

Ardabili,

Bahmani,

Lahijan

et al. 2024

Sensors

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Nowadays, the automatic detection of driver fatigue has become one of the important measures to prevent traffic accidents. For this purpose, a lot of research has been conducted in this field in recent years. However, the diagnosis of fatigue in recent research is binary and has no operational capability. This research presents a multi-class driver fatigue detection system based on electroencephalography (EEG) signals using deep learning networks. In the proposed system, a standard driving simulator has been designed, and a database has been collected based on the recording of EEG signals from 20 participants in five different classes of fatigue. In addition to self-report questionnaires, changes in physiological patterns are used to confirm the various stages of weariness in the suggested model. To pre-process and process the signal, a combination of generative adversarial networks (GAN) and graph convolutional networks (GCN) has been used. The proposed deep model includes five convolutional graph layers, one dense layer, and one fully connected layer. The accuracy obtained for the proposed model is 99%, 97%, 96%, and 91%, respectively, for the four different considered practical cases. The proposed model is compared to one developed through recent methods and research and has a promising performance.

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Section: Related Workmentioning

confidence: 99%

A Novel Approach for Automatic Detection of Driver Fatigue Using EEG Signals Based on Graph Convolutional Networks

Ardabili,

Bahmani,

Lahijan

et al. 2024

Sensors

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“…Islam A. Fouad et al [22] presented a software-based driver fatigue detection system using 32 EEG channels. Employing a preprocessing pipeline consisting of a band-pass filter [0.…”

Section: Literaturementioning

confidence: 99%

“…As previously discussed, EEG signals have emerged as a valuable and precise tool for early drowsiness detection [12]. Characterized by their non-stationary and non-linear nature, EEG signals depict brain activity.…”

Section: Eeg-based Drowsiness Detectionmentioning

confidence: 99%

Efficient Generalized EEG-Based Drowsiness Detection Approach with Minimal Electrodes

Zayed,

Belhadj,

Ben Khalifa

et al. 2024

Preprint

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Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as construction, transportation, industry and medicine, due to the lack of monitoring vigilance in the mentioned areas. The implementation of a drowsiness detection system can greatly help to reduce the defects and accident rates by alerting individuals when they enter a drowsy state. This research proposes an Electroencephalography (EEG) based approach for detecting drowsiness. EEG signals are passed through a preprocessing chain composed of artifact removal and segmentation to ensure accurate detection followed by different feature extraction methods to extract the different features related to drowsiness. This work explores the use of various machine learning algorithms such as Support Vector Machine (SVM) the K Nearest Neighbor (KNN) the Naive Bayes (NB) the Decision Tree (DT) and the Multilayer Perceptron (MLP) to analyze EEG signals sourced from the DROZY database, carefully labeled into two distinct states of alertness (awake, and drowsy). Segmentation into 10-second intervals ensures precise detection, while a relevant feature selection layer enhances accuracy and generalizability. The proposed approach achieves high accuracy rates of 99.84% and 96.4% for intra (subject by subject) and inter (cross-subject) modes, respectively. SVM emerges as the most effective model for drowsiness detection in the intra mode, while MLP demonstrates superior accuracy in the inter mode. This research offers a promising avenue for implementing proactive drowsiness detection systems to enhance occupational safety across various industries.

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“…The basic idea is to solve the separation hyperplane that can correctly divide the training data set and has the largest geometric interval. Its learning strategy maximizes the interval and finally becomes the solution to a convex quadratic programming problem [32]. SVM can be divided into linear separable support vector machines, linear support vector machines, and nonlinear support vector machines.…”

Section: Support Vector Machinementioning

confidence: 99%

Hearing loss classification via AlexNet and Support Vector Machine

Wang

2023

EAI Endorsed Trans AI Robotics

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This paper presents a new method for detecting hearing loss. Our approach is first to use AlexNet to extract the features. Then, we use the Support Vector Machine as a classifier to classify the images. 10-fold cross-validation results showed that the sensitivities of the healthy control group, the left-sided hearing loss group, and the right-sided hearing loss group in this method were 94.67%, 94.00%, and 95.17%, respectively, achieving a very good effect compared with other hearing loss detection methods. In conclusion, our method is effective for the identification of hearing loss.

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A robust and efficient EEG-based drowsiness detection system using different machine learning algorithms

Cited by 27 publications

References 51 publications

A Novel Approach for Automatic Detection of Driver Fatigue Using EEG Signals Based on Graph Convolutional Networks

A Novel Approach for Automatic Detection of Driver Fatigue Using EEG Signals Based on Graph Convolutional Networks

Efficient Generalized EEG-Based Drowsiness Detection Approach with Minimal Electrodes

Hearing loss classification via AlexNet and Support Vector Machine

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