Automatic Detection of Drowsiness in EEG Records Based on Machine Learning Approaches

Abidi, Afef; Khalifa, Khaled Ben; Cheikh, Ridha Ben; Valderrama, Carlos; Bedoui, Mohamed Hédi

doi:10.1007/s11063-022-10858-x

Cited by 12 publications

(4 citation statements)

References 56 publications

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“…

rhythms are dominant in states of light sleep, closed eyes, and relaxation ( Croce et al, 2018 ; Liu et al, 2021 ), while

band is indicative of alertness, focus, and wakefulness states with cognition processes ( Arif et al, 2021a ). The transition from alert to drowsy state can be captured by the transition from

band along with the slow-wave bands (

and

) ( Abidi et al, 2022 ; Li and Chung, 2022 ). The higher magnitude of spectral band powers and PSD estimates in these EEG bands for all the selected channels are analyzed to measure the patterns of alert and drowsy states of drivers.…”

Section: Methodsmentioning

confidence: 99%

Driving drowsiness detection using spectral signatures of EEG-based neurophysiology

Arif

Munawar²,

Ali³

2023

Front. Physiol.

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Introduction: Drowsy driving is a significant factor causing dire road crashes and casualties around the world. Detecting it earlier and more effectively can significantly reduce the lethal aftereffects and increase road safety. As physiological conditions originate from the human brain, so neurophysiological signatures in drowsy and alert states may be investigated for this purpose. In this preface, A passive brain-computer interface (pBCI) scheme using multichannel electroencephalography (EEG) brain signals is developed for spatially localized and accurate detection of human drowsiness during driving tasks.Methods: This pBCI modality acquired electrophysiological patterns of 12 healthy subjects from the prefrontal (PFC), frontal (FC), and occipital cortices (OC) of the brain. Neurological states are recorded using six EEG channels spread over the right and left hemispheres in the PFC, FC, and OC of the sleep-deprived subjects during simulated driving tasks. In post-hoc analysis, spectral signatures of the δ, θ, α, and β rhythms are extracted in terms of spectral band powers and their ratios with a temporal correlation over the complete span of the experiment. Minimum redundancy maximum relevance, Chi-square, and ReliefF feature selection methods are used and aggregated with a Z-score based approach for global feature ranking. The extracted drowsiness attributes are classified using decision trees, discriminant analysis, logistic regression, naïve Bayes, support vector machines, k-nearest neighbors, and ensemble classifiers. The binary classification results are reported with confusion matrix-based performance assessment metrics.Results: In inter-classifier comparison, the optimized ensemble model achieved the best results of drowsiness classification with 85.6% accuracy and precision, 89.7% recall, 87.6% F1-score, 80% specificity, 70.3% Matthews correlation coefficient, 70.2% Cohen’s kappa score, and 91% area under the receiver operating characteristic curve with 76-ms execution time. In inter-channel comparison, the best results were obtained at the F8 electrode position in the right FC of the brain. The significance of all the results was validated with a p-value of less than 0.05 using statistical hypothesis testing methods.Conclusions: The proposed scheme has achieved better results for driving drowsiness detection with the accomplishment of multiple objectives. The predictor importance approach has reduced the feature extraction cost and computational complexity is minimized with the use of conventional machine learning classifiers resulting in low-cost hardware and software requirements. The channel selection approach has spatially localized the most promising brain region for drowsiness detection with only a single EEG channel (F8) which reduces the physical intrusiveness in normal driving operation. This pBCI scheme has a good potential for practical applications requiring earlier, more accurate, and less disruptive drowsiness detection using the spectral information of EEG biosignals.

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“…

rhythms are dominant in states of light sleep, closed eyes, and relaxation ( Croce et al, 2018 ; Liu et al, 2021 ), while

band is indicative of alertness, focus, and wakefulness states with cognition processes ( Arif et al, 2021a ). The transition from alert to drowsy state can be captured by the transition from

band along with the slow-wave bands (

and

Section: Methodsmentioning

confidence: 99%

Driving drowsiness detection using spectral signatures of EEG-based neurophysiology

Arif

Munawar²,

Ali³

2023

Front. Physiol.

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“…Employing KNN and SVM as binary classifiers, their system achieved impressive accuracy rates of 97.2% and 96.4% for the KNN and the SVM, respectively, in the intra mode. Abidi et al [27] introduced a novel approach for drowsiness detection using 10-second segments. Their methodology involved applying the TQWT to extract two EEG subbands, Alpha and Theta, along with nine temporal features.…”

Section: Literaturementioning

confidence: 99%

“…Relative power spectral density The PSD [26,27] algorithm quantifies the power distribution of EEG signals across predefined frequency bands, typically ranging from 0.1 to 30 Hz for hypovigilance studies.…”

mentioning

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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“…Paulo et al (2021) proposed using recursive graphs and gramian angular fields to transform the raw EEG signals into image-like data, which is then input into a single-layer convolutional neural network (CNN) to achieve fatigue detection. Abidi et al (2022) processed the raw EEG signals using a tunable Q-factor wavelet transform and extracted signal features using kernel principal component analysis (KPCA). They then used k-nearest neighbors (KNN) and support vector machine (SVM) for EEG signal classification.…”

Section: Introductionmentioning

confidence: 99%

Attention-based multi-semantic dynamical graph convolutional network for eeg-based fatigue detection

Liu,

Cai

et al. 2023

Front. Neurosci.

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IntroductionEstablishing a driving fatigue monitoring system is of utmost importance as severe fatigue may lead to unimaginable consequences. Fatigue detection methods based on physiological information have the advantages of reliable and accurate. Among various physiological signals, EEG signals are considered to be the most direct and promising ones. However, most traditional methods overlook the functional connectivity of the brain and fail to meet real-time requirements.MethodsTo this end, we propose a novel detection model called Attention-Based Multi-Semantic Dynamical Graph Convolutional Network (AMD-GCN). AMD-GCN consists of a channel attention mechanism based on average pooling and max pooling (AM-CAM), a multi-semantic dynamical graph convolution (MD-GC), and a spatial attention mechanism based on average pooling and max pooling (AM-SAM). AM-CAM allocates weights to the input features, helping the model focus on the important information relevant to fatigue detection. MD-GC can construct intrinsic topological graphs under multi-semantic patterns, allowing GCN to better capture the dependency between physically connected or non-physically connected nodes. AM-SAM can remove redundant spatial node information from the output of MD-GC, thereby reducing interference in fatigue detection. Moreover, we concatenate the DE features extracted from 5 frequency bands and 25 frequency bands as the input of AMD-GCN.ResultsFinally, we conduct experiments on the public dataset SEED-VIG, and the accuracy of AMD-GCN model reached 89.94%, surpassing existing algorithms.DiscussionThe findings indicate that our proposed strategy performs more effectively for EEG-based driving fatigue detection.

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Automatic Detection of Drowsiness in EEG Records Based on Machine Learning Approaches

Cited by 12 publications

References 56 publications

Driving drowsiness detection using spectral signatures of EEG-based neurophysiology

Driving drowsiness detection using spectral signatures of EEG-based neurophysiology

Efficient Generalized EEG-Based Drowsiness Detection Approach with Minimal Electrodes

Attention-based multi-semantic dynamical graph convolutional network for eeg-based fatigue detection

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