Research on fatigue driving detection using forehead EEG based on adaptive multi-scale entropy

Causal brain network analysis of driving fatigue based on generalized orthogonalized partially directed coherence

Driving fatigue is a serious threat to driving safety. Therefore, it is of great significance to accurately detect driving fatigue. In this study, the generalized orthogonal partial directed coherence (gOPDC) algorithm, which measures the time-frequency domain interaction of electroencephalogram (EEG) signals, was used to accurately estimate the connectivity between cortical channels. The causal brain network of driver continuous driving is constructed. The results show that the clustering coefficient and global efficiency tend to decrease with the increase in driving time. Causal information flow in the left prefrontal, parietal, occipital regions and the right posterior frontal region increased significantly when subjects transitioned from awake to fatigued, while causal information flow in the right prefrontal, parietal, occipital regions and the left posterior frontal region decreased mutually significantly. Compared with the traditional driving fatigue algorithm, the accuracy of the method used in this paper is higher than the traditional methods.

Fatigue Characterization of EEG Brain Networks Under Mixed Reality Stereo Vision

Mixed Reality (MR) technology possesses profound and extensive potential across a multitude of domains, including, but not limited to industry, healthcare, and education. However, prolonged use of MR devices to watch stereoscopic content may lead to visual fatigue. Since visual fatigue involves multiple brain regions, our study aims to explore the topological characteristics of brain networks derived from electroencephalogram (EEG) data. Because the Phase-Locked Value (PLV) is capable of effectively measuring the phase synchronization relationship between brain regions, it was calculated between all pairs of channels in both comfort and fatigue states. Subsequently, a sparse brain network was constructed based on PLV by applying an appropriate threshold. The node properties (betweenness centrality, clustering coefficient, node efficiency) and edge properties (characteristic path length) were calculated based on the corresponding brain network within specific frequency bands for both comfort and fatigue states. In analyzing the PLV of brain connectivity in comfort and fatigue states, a notable enhancement in brain connectivity is observed within the alpha, theta, and delta frequency bands during fatigue status. By analyzing the node and edge properties of brain networks, it is evident that the mean values of these properties in the fatigue state were higher than those in the comfort state. By analyzing the node and edge properties at a local level, the average difference in betweenness centrality, clustering coefficients, and nodal efficiency across the three EEG frequency bands was computed to find significant brain regions. The main findings are as follows: Betweenness centrality primarily differs in frontal and parietal regions, with minor involvement in temporal and central regions. The clustering Coefficient mainly varies in the frontal region, with slight differences being seen in the temporal and occipital regions. Nodal efficiency primarily varies in the frontal, temporal, and central regions, with minor differences being seen in the parietal and occipital regions. Edge property analysis indicates that there is a higher occurrence of long-distance connections among brain regions during the fatigue state, which reflects a loss of synaptic transmission efficiency on a global level. Our study plays a crucial role in understanding the neural mechanisms underlying visual fatigue, potentially providing insights that could be applied to high-demand cognitive fields where prolonged use of MR devices leads to visual fatigue.

Real-time EEG-based detection of driving fatigue using a novel semi-dry electrode with self-replenishment of conductive fluid

A novel semi-dry electrode that can realize self-replenishment of conductive liquid is proposed in this study. Driving fatigue is detected by extracting the refined composite multiscale fluctuation dispersion entropy (RCMFDE) features in electroencephalogram (EEG) signals collected by this electrode. The results show that the new semi-dry electrode can automatically complete the conductive fluid supplement according to its own humidity conditions, which not only notably improves the effective working time, but also significantly reduces the skin impedance. By comparing with the classical entropy algorithms, the computational speed and the stability of the RCMFDE method are Substantially enhanced.

An improved CapsNet based on data augmentation for driver vigilance estimation with forehead single-channel EEG

Various studies have shown that it is necessary to estimate the drivers' vigilance to reduce the occurrence of traffic accidents. Most existing EEG-based vigilance estimation studies have been performed on intra-subject and multi-channel signals, and these methods are too costly and complicated to implement in practice. Hence, aiming at the problem of cross-subject vigilance estimation of single-channel EEG signals, an estimation algorithm based on capsule network (CapsNet) is proposed. Firstly, we propose a new construction method of the input feature maps to fit the characteristics of CapsNet to improve the algorithm accuracy. Meanwhile, the self-attention mechanism is incorporated in the algorithm to focus on the key information in feature maps. Secondly, we propose substituting the traditional multi-channel signals with the single-channel signals to improve the utility of algorithm. Thirdly, since the single-channel signals carry fewer dimensions of the information compared to the multi-channel signals, we use the conditional generative adversarial network to improve the accuracy of single-channel signals by increasing the amount of data. The proposed algorithm is verified on the SEED-VIG, and Root-mean-square-error (RMSE) and Pearson Correlation Coefficient (PCC) are used as the evaluation metrics. The results show that the proposed algorithm improves the computing speed while the RMSE is reduced by 3%, and the PCC is improved by 12% compared to the mainstream algorithm. Experiment results prove the feasibility of using forehead single-channel EEG signals for cross-subject vigilance estimation and offering the possibility of lightweight EEG vigilance estimation devices for practical applications.

Research on low-power driving fatigue monitoring method based on spiking neural network

Fatigue driving is one of the leading causes of traffic accidents, and the rapid and accurate detection of driver fatigue is of paramount importance for enhancing road safety. However, the application of deep learning models in fatigue driving detection has long been constrained by high computational costs and power consumption. To address this issue, this study proposes an approach that combines Self-Organizing Map (SOM) and Spiking Neural Networks (SNN) to develop a low-power model capable of accurately recognizing the driver's mental state. Initially, spatial features are extracted from electroencephalogram (EEG) signals using the SOM network. Subsequently, the extracted weight vectors are encoded and fed into the SNN for fatigue driving classification. The research results demonstrate that the proposed method effectively considers the spatiotemporal characteristics of EEG signals, achieving efficient fatigue detection. Simultaneously, this approach successfully reduces the model's power consumption. When compared to traditional artificial neural networks, our method reduces energy consumption by approximately 12.21-42.59%.

Designing a practical fatigue detection system: A review on recent developments and challenges

INTRODUCTION

Fatigue is considered to have a life-threatening effect on human health and it has been an active field of research in different sectors. Deploying wearable physiological sensors helps to detect the level of fatigue objectively without any concern of bias in subjective assessment and interfering with work.

METHODS

This paper provides an in-depth review of fatigue detection approaches using physiological signals to pinpoint their main achievements, identify research gaps, and recommend avenues for future research. The review results are presented under three headings, including: signal modality, experimental environments, and fatigue detection models. Fatigue detection studies are first divided based on signal modality into uni-modal and multi-modal approaches. Then, the experimental environments utilized for fatigue data collection are critically analyzed. At the end, the machine learning models used for the classification of fatigue state are reviewed.

PRACTICAL APPLICATIONS

The directions for future research are provided based on critical analysis of past studies. Finally, the challenges of objective fatigue detection in the real-world scenario are discussed.

Cognitive Workload Detection of Air Traffic Controllers Based on mRMR and Fewer EEG Channels

For air traffic controllers, the extent of their cognitive workload can significantly impact their cognitive function and response time, consequently influencing their operational efficiency or even resulting in safety incidents. In order to enhance the accuracy and efficiency in determining the cognitive workload of air traffic controllers, a cognitive workload detection method for air traffic controllers based on mRMR and fewer EEG channels was proposed in this study. First of all, a set of features related to gamma waves was initially proposed; subsequently, an EEG feature evaluation method based on the mRMR algorithm was employed to pinpoint the most relevant indicators for the detection of the cognitive workload. Consequently, a model for the detection of the cognitive workload of controllers was developed, and it was optimized by filtering out channel combinations that exhibited higher sensitivity to the workload using the mRMR algorithm. The results demonstrate that the enhanced model achieves the accuracy and stability required for practical applications. Notably, in this study, only three EEG channels were employed to achieve the highly precise detection of the cognitive workload of controllers. This approach markedly increases the practicality of employing EEG equipment for the detection of the cognitive workload and streamlines the detection process.

Driving fatigue detection based on brain source activity and ARMA model

Fatigue among drivers is a significant issue in society, and according to organizational reports, it substantially contributes to accidents. So accurate fatigue detection in drivers plays a crucial role in reducing the number of people fatalities or injured resulting from accidents. Several methods are proposed for fatigue driver recognition among which electroencephalography (EEG) is one. This paper proposed a method for fatigue recognition by EEG signals with extracted features from source and sensor spaces. The proposed method starts with preprocessing by applying filtering and artifact rejection. Then source localization methods are applied to EEG signals for active source extraction. A multivariate autoregressive (MVAR) model is fitted to selected sources, and a dual Kalman filter is applied to estimate the source activity and their relationships. Then multivariate autoregressive moving average (ARMA) is fitted between EEG and source activity signals. Features are extracted from model parameters, source relationship matrix, and wavelet transform of EEG and source activity signals. The novelty of this approach is the use of ARMA model between source activities (as input) and EEG signals (as output) and feature extraction from source relations. Relevant features are selected using a combination of RelifF and neighborhood component analysis (NCA) methods. Three classifiers, namely k-nearest neighbor (KNN), support vector machine (SVM), and naive Bayesian (NB) classifiers, are employed to classify drivers. To improve performance, the final label for fatigue detection is calculated by combining these classifiers using the voting method. The results demonstrate that the proposed method accurately recognizes and classifies fatigued drivers with the ensemble classifiers in comparison with other methods.

Deep Learning for Detecting Multi-Level Driver Fatigue Using Physiological Signals: A Comprehensive Approach

A large share of traffic accidents is related to driver fatigue. In recent years, many studies have been organized in order to diagnose and warn drivers. In this research, a new approach was presented in order to detect multi-level driver fatigue. A multi-level driver tiredness diagnostic database based on physiological signals including ECG, EEG, EMG, and respiratory effort was developed for this aim. The EEG signal was used for processing and other recorded signals were used to confirm the driver's fatigue so that fatigue was not confirmed based on self-report questionnaires. A customized architecture based on adversarial generative networks and convolutional neural networks (end-to-end) was utilized to select/extract features and classify different levels of fatigue. In the customized architecture, with the objective of eliminating uncertainty, type 2 fuzzy sets were used instead of activation functions such as Relu and Leaky Relu, and the performance of each was investigated. The final accuracy obtained in the three scenarios considered, two-level, three-level, and five-level, were 96.8%, 95.1%, and 89.1%, respectively. Given the suggested model's optimal performance, which can identify five various levels of driver fatigue with high accuracy, it can be employed in practical applications of driver fatigue to warn drivers.

An EEG-based attention recognition method: fusion of time domain, frequency domain, and non-linear dynamics features

Introduction

Attention is a complex cognitive function of human brain that plays a vital role in our daily lives. Electroencephalogram (EEG) is used to measure and analyze attention due to its high temporal resolution. Although several attention recognition brain-computer interfaces (BCIs) have been proposed, there is a scarcity of studies with a sufficient number of subjects, valid paradigms, and reliable recognition analysis across subjects.

Methods

In this study, we proposed a novel attention paradigm and feature fusion method to extract features, which fused time domain features, frequency domain features and nonlinear dynamics features. We then constructed an attention recognition framework for 85 subjects.

Results and discussion

We achieved an intra-subject average classification accuracy of 85.05% ± 6.87% and an inter-subject average classification accuracy of 81.60% ± 9.93%, respectively. We further explored the neural patterns in attention recognition, where attention states showed less activation than non-attention states in the prefrontal and occipital areas in α, β and θ bands. The research explores, for the first time, the fusion of time domain features, frequency domain features and nonlinear dynamics features for attention recognition, providing a new understanding of attention recognition.

Automatic detection method of epileptic seizures based on IRCMDE and PSO-SVM

Multi-scale dispersion entropy (MDE) has been widely used to extract nonlinear features of electroencephalography (EEG) signals and realize automatic detection of epileptic seizures. However, information loss and poor robustness will exist when MDE is used to measure the nonlinear complexity of the time sequence. To solve the above problems, an automatic detection method for epilepsy was proposed, based on improved refined composite multi-scale dispersion entropy (IRCMDE) and particle swarm algorithm optimization support vector machine (PSO-SVM). First, the refined composite multi-scale dispersion entropy (RCMDE) is introduced, and then the segmented average calculation of coarse-grained sequence is replaced by local maximum calculation to solve the problem of information loss. Finally, the entropy value is normalized to improve the robustness of characteristic parameters, and IRCMDE is formed. The simulated results show that when examining the complexity of the simulated signal, IRCMDE can eliminate the issue of information loss compared with MDE and RCMDE and weaken the entropy change caused by different parameter selections. In addition, IRCMDE is used as the feature parameter of the epileptic EEG signal, and PSO-SVM is used to identify the feature parameters. Compared with MDE-PSO-SVM, and RCMDE-PSO-SVM methods, IRCMDE-PSO-SVM can obtain more accurate recognition results.

Investigating immunosensor for determination of SD-biomarker-Aβ based on AuNPs/AC@PANI@CS modified electrodes with amplifying the signal

Sleep debt (SD) is one of the important triggers for causing not only physiological and mental illness but also dangerous work. Therefore, achieving an early and objective assessment of SD is of great significance in the precaution against SD-related diseases and unsafe work. Here, an ultrasensitive electrochemical immunosensor was constructed for analysis of SD biomarker amyloid-β (Aβ). The gold nanoparticles/chitosan-coated polyaniline-functionalized activated carbon (AuNPs/AC@PANI@CS) composites were employed as the sensing platforms. Since PANI and AC can form an effective conductive path, it can effectively enhance the penetration of electrolytes on the electrode surface and the rapid transport of charges and ions, significantly enhancing the electrochemical response signal of the immunosensor. Under the optimized experimental conditions, the fabricated immunosensor had a wide linear range of 1.95 pg mL^-1 to 1000.00 pg mL^-1, with a low detection limit of 0.014 pg mL^-1. This study not only provides an effective method for the accurate and rapid detection of Aβ, but also offers a novel evaluation strategy for the objective assessment of SD and the study of related pathological mechanisms.

Driving Fatigue Detection with Three Non-Hair-Bearing EEG Channels and Modified Transformer Model

Driving fatigue is the main cause of traffic accidents, which seriously affects people's life and property safety. Many researchers have applied electroencephalogram (EEG) signals for driving fatigue detection to reduce negative effects. The main challenges are the practicality and accuracy of the EEG-based driving fatigue detection method when it is applied on the real road. In our previous study, we attempted to improve the practicality of fatigue detection based on the proposed non-hair-bearing (NHB) montage with fewer EEG channels, but the recognition accuracy was only 76.47% with the random forest (RF) model. In order to improve the accuracy with NHB montage, this study proposed an improved transformer architecture for one-dimensional feature vector classification based on introducing the Gated Linear Unit (GLU) in the Attention sub-block and Feed-Forward Networks (FFN) sub-block of a transformer, called GLU-Oneformer. Moreover, we constructed an NHB-EEG-based feature set, including the same EEG features (power ratio, approximate entropy, and mutual information (MI)) in our previous study, and the lateralization features of the power ratio and approximate entropy based on the strategy of brain lateralization. The results indicated that our GLU-Oneformer method significantly improved the recognition performance and achieved an accuracy of 86.97%. Our framework demonstrated that the combination of the NHB montage and the proposed GLU-Oneformer model could well support driving fatigue detection.

A New Feature Analysis Approach to Selecting Channels of EEG for Fatigue Driving

Fatigued driving is a significant contributor to traffic accidents. There are some issues with common EEG data of 32 channels, 64 channels, and 128 channels, such as difficult acquisition, high data redundancy, and difficult practical application. A new channel selection method called ReliefF_SFS is proposed to address the problem of how to reduce the number of channels while maintaining classification accuracy. It combines the ReliefF algorithm and the sequential forward selection (SFS) algorithm. When only T6, O1, Oz, T4, P3, and FC3 are used, the classification accuracy under Theta_Std+FE combined with ReliefF_SFS achieves 99.45%. The strategy suggested in this paper not only ensures the recognition accuracy but also reduces the number of channels when compared to other models based on the same data set.

Automatic Detection of Driver Fatigue Based on EEG Signals Using a Developed Deep Neural Network

In recent years, detecting driver fatigue has been a significant practical necessity and issue. Even though several investigations have been undertaken to examine driver fatigue, there are relatively few standard datasets on identifying driver fatigue. For earlier investigations, conventional methods relying on manual characteristics were utilized to assess driver fatigue. In any case study, such approaches need previous information for feature extraction, which could raise computing complexity. The current work proposes a driver fatigue detection system, which is a fundamental necessity to minimize road accidents. Data from 11 people are gathered for this purpose, resulting in a comprehensive dataset. The dataset is prepared in accordance with previously published criteria. A deep convolutional neural network–long short-time memory (CNN–LSTM) network is conceived and evolved to extract characteristics from raw EEG data corresponding to the six active areas A, B, C, D, E (based on a single channel), and F. The study’s findings reveal that the suggested deep CNN–LSTM network could learn features hierarchically from raw EEG data and attain a greater precision rate than previous comparative approaches for two-stage driver fatigue categorization. The suggested approach may be utilized to construct automatic fatigue detection systems because of their precision and high speed.

Research on driving fatigue detection based on basic scale entropy and MVAR-PSI

In long-term continuous driving, driving fatigue is the main cause of traffic accidents. Therefore, accurate and rapid detection of driver mental fatigue is of great significance to traffic safety. In our study, the electroencephalogram (EEG) signals of subjects were preprocessed to remove interference signals. The Butterworth band-pass filter is used to extract the EEG signals ofαandβrhythms, and then the basic scale entropy ofαandβrhythms is used as driving fatigue characteristics. In addition, combined with the fast multiple autoregressive (MVAR) model and phase slope index (PSI), short-term data is used to accurately estimate the effective connectivity of EEG signals between different channels, and analyzed the causality flow direction in the left and right prefrontal regions of drivers at different driving stages. Further comprehensive analysis of the driver's driving fatigue state in the continuous driving phase. Finally, the correlation coefficient value between the parameter pairs (basic scale entropy, clustering coefficient, global efficiency) is calculated. The results showed that the causality flow outflow degree of prefrontal lobe decreased during the transition from sober driving state to tired driving state. The left and right prefrontal lobes were the source of causality in sober driving state, and gradually became the target of causality with the occurrence of driving fatigue. The results showed that when transitioning from a waking state to a fatigued driving state, the causal flow direction out-degree value of the prefrontal cortex on a declining curve, and the left and right prefrontal cortex exhibited the causal source in the awake driving state, which gradually changed into the causal target along with the occurrence of driving fatigue. The three parameters of basic scale entropy, clustering coefficient and global efficiency are used as driving fatigue characteristics, and every two parameters have strong correlation. It shows that the combination of basic scale entropy and MVAR-PSI method can effectively detect the driver's long-term driving fatigue state in continuous driving mode.

A Study of the Effects of Different Indoor Lighting Environments on Computer Work Fatigue

The indoor lighting environment is a key factor affecting human health and safety. In particular, people have been forced to study or work more for long periods of time at home due to the COVID-19 pandemic. In this study, we investigate the influence of physical indoor environmental factors, correlated color temperature (CCT), and illumination on computer work fatigue. We conducted a within-subject experiment consisting of a 10 min-long task test under two different illumination settings (300 lx and 500 lx) and two CCTs (3000 K and 4000 K). Physiological signals, such as electroencephalogram (EEG), electrocardiograph (ECG), and eye movement, were monitored during the test to objectively measure fatigue. The subjective fatigue of eight participants was evaluated based on a questionnaire conducted after completing the test. The error rate of the task test was taken as the key factor representing the working performance. Through the analysis of the subjective and objective results, computer work fatigue was found to be significantly impacted by changes in the lighting environment, where human fatigue was negatively correlated with illumination and CCT. Improving the illumination and CCT of the work environment, within the scope of this study, helped to decrease the fatigue degree—that is, the fatigue degree was the lowest under the 4000 K + 500 lx environment, while it was relatively high at 3000 K + 300 lx. Under indoor environment conditions, the CCT factor was found to have the greatest effect on computer work fatigue, followed by illumination. The presented results are expected to be a valuable reference for improving the satisfaction associated with the lighting environment and to serve as guidance for researchers and reviewers conducting similar research.

Motion Fatigue State Detection Based on Neural Networks

Aiming at the problem of fatigue state detection at the back of sports, a cascade deep learning detection system structure is designed, and a convolutional neural network fatigue state detection model based on multiscale pooling is proposed. Firstly, face detection is carried out by a deep learning model MTCNN to extract eye and mouth regions. Aiming at the problem of eye and mouth state representation and recognition, a multiscale pooling model (MSP) based on RESNET  is proposed to train the eye and mouth state. In real-time detection, the state of the eye and mouth region is recognized through the trained convolution neural network model. Finally, the athlete's fatigue is determined based on PERCLOS and the proposed mouth opening and closing frequency (FOM). The experimental results show that in the training process, we set the batch_size = 100 and the initial learning rate = 0.01. When the evaluation index is no longer improved, the learning rate is reduced by 10 times to 0.001, and a total of 50 epochs are trained. The precision and recall of the system are high. Compared with the infrared image simulating the night state, the RGB image taken by the ordinary camera in the daytime has higher precision and recall. It is proven that the neural network has high detection accuracy, meets the real-time requirements, and has high robustness in complex environments.

Developing a Deep Neural Network for Driver Fatigue Detection Using EEG Signals Based on Compressed Sensing

In recent years, driver fatigue has become one of the main causes of road accidents. As a result, fatigue detection systems have been developed to warn drivers, and, among the available methods, EEG signal analysis is recognized as the most reliable method for detecting driver fatigue. This study presents an automated system for a two-stage classification of driver fatigue, using a combination of compressed sensing (CS) theory and deep neural networks (DNNs), that is based on EEG signals. First, CS theory is used to compress the recorded EEG data in order to reduce the computational load. Then, the compressed EEG data is fed into the proposed deep convolutional neural network for automatic feature extraction/selection and classification purposes. The proposed network architecture includes seven convolutional layers together with three long short-term memory (LSTM) layers. For compression rates of 40, 50, 60, 70, 80, and 90, the simulation results for a single-channel recording show accuracies of 95, 94.8, 94.6, 94.4, 94.4, and 92%, respectively. Furthermore, by comparing the results to previous methods, the accuracy of the proposed method for the two-stage classification of driver fatigue has been improved and can be used to effectively detect driver fatigue.

SSA with CWT and k-Means for Eye-Blink Artifact Removal from Single-Channel EEG Signals

Recently, the use of portable electroencephalogram (EEG) devices to record brain signals in both health care monitoring and in other applications, such as fatigue detection in drivers, has been increased due to its low cost and ease of use. However, the measured EEG signals always mix with the electrooculogram (EOG), which are results due to eyelid blinking or eye movements. The eye-blinking/movement is an uncontrollable activity that results in a high-amplitude slow-time varying component that is mixed in the measured EEG signal. The presence of these artifacts misled our understanding of the underlying brain state. As the portable EEG devices comprise few EEG channels or sometimes a single EEG channel, classical artifact removal techniques such as blind source separation methods cannot be used to remove these artifacts from a single-channel EEG signal. Hence, there is a demand for the development of new single-channel-based artifact removal techniques. Singular spectrum analysis (SSA) has been widely used as a single-channel-based eye-blink artifact removal technique. However, while removing the artifact, the low-frequency components from the non-artifact region of the EEG signal are also removed by SSA. To preserve these low-frequency components, in this paper, we have proposed a new methodology by integrating the SSA with continuous wavelet transform (CWT) and the k-means clustering algorithm that removes the eye-blink artifact from the single-channel EEG signals without altering the low frequencies of the EEG signal. The proposed method is evaluated on both synthetic and real EEG signals. The results also show the superiority of the proposed method over the existing methods.

A Memristive Circuit Implementation of Eyes State Detection in Fatigue Driving Based on Biological Long Short-Term Memory Rule

Biological long short-term memory (B-LSTM) can effectively help human process all kinds of received information. In this work, a memristive B-LSTM circuit which mimics a conversion from short-term memory to long-term memory is proposed. That is, the stronger the signal, the more profound the memory and the higher the output. On this basis, an image binarization circuit using adaptive row threshold algorithm is proposed. It can make the image remain a deep impression on the strong pixel information and effectively filter the relatively weak pixel information. In combination with the function of image binarization, a memristive circuit for eyes state detection is proposed by adding corresponding horizontal projection calculation, subtraction calculation and judgement open or closed eyes modules. The proposed circuit can detect whether there is a blink between two adjacent facial images, which uses the characteristics of memristor to detect the difference of horizontal projection between two images. Due to the use of memristor, the proposed circuit can realize in-memory computing, which fundamentally avoids the problem of storage wall and shorten the execution time. Finally, an expectation application in fatigue driving based on the proposed method is demonstrated, which indicates the practicability of the circuit design in this work.

Research on Driving Fatigue Alleviation Using Interesting Auditory Stimulation Based on VMD-MMSE

The accurate detection and alleviation of driving fatigue are of great significance to traffic safety. In this study, we tried to apply the modified multi-scale entropy (MMSE) approach, based on variational mode decomposition (VMD), to driving fatigue detection. Firstly, the VMD was used to decompose EEG into multiple intrinsic mode functions (IMFs), then the best IMFs and scale factors were selected using the least square method (LSM). Finally, the MMSE features were extracted. Compared with the traditional sample entropy (SampEn), the VMD-MMSE method can identify the characteristics of driving fatigue more effectively. The VMD-MMSE characteristics combined with a subjective questionnaire (SQ) were used to analyze the change trends of driving fatigue under two driving modes: normal driving mode and interesting auditory stimulation mode. The results show that the interesting auditory stimulation method adopted in this paper can effectively relieve driving fatigue. In addition, the interesting auditory stimulation method, which simply involves playing interesting auditory information on the vehicle-mounted player, can effectively relieve driving fatigue. Compared with traditional driving fatigue-relieving methods, such as sleeping and drinking coffee, this interesting auditory stimulation method can relieve fatigue in real-time when the driver is driving normally.

Simultaneous Eye Blink Characterization and Elimination from Low-Channel Prefrontal EEG Signals Enhances Driver Drowsiness Detection

OBJECTIVE

Blink-related features derived from electroencephalography (EEG) have recently arisen as a meaningful measure of drivers cognitive state. Combined with band power features of low-channel prefrontal EEG data, blink-derived features enhance the detection of driver drowsiness. Yet, it remains unanswered whether synergy of combined blink and EEG band power features for the detection of driver drowsiness may be further boosted if a proper eye blink removal is also applied before EEG analysis. This paper proposes an algorithm for simultaneous eye blink feature extraction and elimination from low-channel prefrontal EEG data.

METHODS

Firstly, eye blink intervals (EBIs) are identified from the Fp1 EEG channel using variational mode extraction, and then blink-related features are derived. Secondly, the identified EBIs are projected to the rest of EEG channels and then filtered by a combination of principal component analysis and discrete wavelet transform. Thirdly, a support vector machine with 10-fold cross-validation is employed to classify alert and drowsy states from the derived blink and filtered EEG band power features.

MAIN RESULTS

When compared the synergy of eye blink and EEG features before and after filtering by the proposed algorithm, a significant improvement in the mean accuracy of driver drowsiness detection was achieved (71.2% vs. 78.1%, p<0.05).

SIGNIFICANCE

This paper validates a novel view of eye blinks as both a source of information and artifacts in EEG-based driver drowsiness detection.

Applications of brain imaging methods in driving behaviour research

Applications of neuroimaging methods have substantially contributed to the scientific understanding of human factors during driving by providing a deeper insight into the neuro-cognitive aspects of driver brain. This has been achieved by conducting simulated (and occasionally, field) driving experiments while collecting driver brain signals of various types. Here, this sector of studies is comprehensively reviewed at both macro and micro scales. At the macro scale, bibliometric aspects of these studies are analysed. At the micro scale, different themes of neuroimaging driving behaviour research are identified and the findings within each theme are synthesised. The surveyed literature has reported on applications of four major brain imaging methods. These include Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS) and Magnetoencephalography (MEG), with the first two being the most common methods in this domain. While collecting driver fMRI signal has been particularly instrumental in studying neural correlates of intoxicated driving (e.g. alcohol or cannabis) or distracted driving, the EEG method has been predominantly utilised in relation to the efforts aiming at development of automatic fatigue/drowsiness detection systems, a topic to which the literature on neuro-ergonomics of driving particularly has shown a spike of interest within the last few years. The survey also reveals that topics such as driver brain activity in semi-automated settings or neural activity of drivers with brain injuries or chronic neurological conditions have by contrast been investigated to a very limited extent. Potential topics in driving behaviour research are identified that could benefit from the adoption of neuroimaging methods in future studies. In terms of practicality, while fMRI and MEG experiments have proven rather invasive and technologically challenging for adoption in driving behaviour research, EEG and fNIRS applications have been more diverse. They have even been tested beyond simulated driving settings, in field driving experiments. Advantages and limitations of each of these four neuroimaging methods in the context of driving behaviour experiments are outlined in the paper.

A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG

Driver drowsiness is one of the main factors leading to road fatalities and hazards in the transportation industry. Electroencephalography (EEG) has been considered as one of the best physiological signals to detect drivers' drowsy states, since it directly measures neurophysiological activities in the brain. However, designing a calibration-free system for driver drowsiness detection with EEG is still a challenging task, as EEG suffers from serious mental and physical drifts across different subjects. In this paper, we propose a compact and interpretable Convolutional Neural Network (CNN) to discover shared EEG features across different subjects for driver drowsiness detection. We incorporate the Global Average Pooling (GAP) layer in the model structure, allowing the Class Activation Map (CAM) method to be used for localizing regions of the input signal that contribute most for classification. Results show that the proposed model can achieve an average accuracy of 73.22% on 11 subjects for 2-class cross-subject EEG signal classification, which is higher than conventional machine learning methods and other state-of-art deep learning methods. It is revealed by the visualization technique that the model has learned biologically explainable features, e.g., Alpha spindles and Theta burst, as evidence for the drowsy state. It is also interesting to see that the model uses artifacts that usually dominate the wakeful EEG, e.g., muscle artifacts and sensor drifts, to recognize the alert state. The proposed model illustrates a potential direction to use CNN models as a powerful tool to discover shared features related to different mental states across different subjects from EEG signals.

A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection utilizing EEG signals

Driver fatigue is the one of the main reasons of the traffic accidents. The human brain is a complex structure, whose function can be evaluated with electroencephalogram (EEG). Automated driver fatigue detection utilizing EEG decreases the incidence probability of related traffic accidents. Therefore, devising an appropriate feature extraction technique and selecting a competent classification method can be considered as the crucial part of the effective driver fatigue detection. Therefore, in this study, an EEG-based intelligent system was devised for driver fatigue detection. The proposed framework includes a new feature generation network, which is implemented by using texture descriptors, for fatigue detection. The proposed scheme contains pre-processing, feature generation, informative features selection and classification with shallow classifiers phases. In the pre-processing, discrete cosine transform and fast Fourier transform are used together. Moreover, dynamic center based binary pattern and multi threshold ternary pattern are utilized together to create a new feature generation network. To improve the detection performance, we utilized discrete wavelet transform as a pooling method, in which the functional brain network-based feature describing the relationship between fatigue and brain network organization. In the feature selection phase, a hybrid three layered feature selection method is presented, and benchmark classifiers are used in the classification phase to demonstrate the strength of the proposed method. In the experiments, the proposed framework achieved 97.29% classification accuracy for fatigue detection using EEG signals. This result reveals that the proposed framework can be utilized effectively for driver fatigue detection.

Feature extraction of EEG signals based on functional data analysis and its application to recognition of driver fatigue state

OBJECTIVE

Our objective is to study how to obtain features which can reflect the continuity and internal dynamic changes of electroencephalography (EEG) signals and study an effective method for fatigued driving state recognition based on the obtained features.

APPROACH

A method of EEG signalfeature extraction based on functional data analysis is proposed. Combined with kernel principal component analysis method, the obtained features are applied to the recognition of driver fatigue state, and a corresponding recognition model of fatigued driving state is constructed.

MAIN RESULTS

The recognition model is tested on the real collected driver fatigue EEG signals by selecting a suitable classifier. The test results show that the proposed driver fatigue state recognition method has good recognition effect, especially on the classifier based on decision tree, with an average accuracy of 99.50%.

SIGNIFICANCE

The extracted features well reflect the continuityand internal dynamic changes of the EEG signals, and it is of great significance and application value to study an effective method of fatigued driver state recognition based on the features.

Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

Internet of things (IoT) cloud-based applications deliver advanced solutions for smart cities to decrease traffic accidents caused by driver fatigue while driving on the road. Environmental conditions or driver behavior can ultimately lead to serious roadside accidents. In recent years, the authors have developed many low-cost, computerized, driver fatigue detection systems (DFDs) to help drivers, by using multi-sensors, and mobile and cloud-based computing architecture. To promote safe driving, these are the most current emerging platforms that were introduced in the past. In this paper, we reviewed state-of-the-art approaches for predicting unsafe driving styles using three common IoT-based architectures. The novelty of this article is to show major differences among multi-sensors, smartphone-based, and cloud-based architectures in multimodal feature processing. We discussed all of the problems that machine learning techniques faced in recent years, particularly the deep learning (DL) model, to predict driver hypovigilance, especially in terms of these three IoT-based architectures. Moreover, we performed state-of-the-art comparisons by using driving simulators to incorporate multimodal features of the driver. We also mention online data sources in this article to test and train network architecture in the field of DFDs on public available multimodal datasets. These comparisons assist other authors to continue future research in this domain. To evaluate the performance, we mention the major problems in these three architectures to help researchers use the best IoT-based architecture for detecting DFDs in a real-time environment. Moreover, the important factors of Multi-Access Edge Computing (MEC) and 5th generation (5G) networks are analyzed in the context of deep learning architecture to improve the response time of DFD systems. Lastly, it is concluded that there is a research gap when it comes to implementing the DFD systems on MEC and 5G technologies by using multimodal features and DL architecture.

Study on the Effect of Man-Machine Response Mode to Relieve Driving Fatigue Based on EEG and EOG

Rapid and accurate detection of driver fatigue is of great significance to improve traffic safety. In the present work, we propose the man-machine response mode (MRM) to relieve driver fatigue caused by long-term driving. In this paper, the characteristics of the complex brain network, which can effectively reflect brain activity information, were used to detect the change of driving fatigue over time. Combined with the traditional eye movement characteristics and a subjective questionnaire (SQ), the changes in driving fatigue characteristics were comprehensively analyzed. The results show that driving fatigue can be effectively delayed using the MRM. Additionally, the response equipment is low in cost and practical, so it will be practical to use in actual driving situations in the future.