EEG-Based Neurocognitive Metrics May Predict Simulated and On-Road Driving Performance in Older Drivers

Predicting collision cases at unsignalized intersections using EEG metrics and driving simulator platform

Unsignalized intersection collision has been one of the most dangerous accidents in the world. How to identify road hazards and predict the potential intersection collision ahead are challenging problems in traffic safety. This paper studies the feasibility of EEG metrics to forecast road hazards and presents an improved neural network model to predict intersection collision based on EEG metrics and driving behavior. It is demonstrated that EEG metrics show significant differences between collision and non-collision cases. It indicates that EEG metrics can serve as effective indicators to predict the collision probability. The drivers with higher relative power in fast frequency band (alpha and beta), lower relative power in slow frequency band (delta and theta) are more likely to have conflicts. The prediction using three machine learning models (Multi-layer perceptron (MLP), Logistic regression (LR) and Random forest (RF)) based on three input datasets (only EEG metrics, only driving behavior and combined EEG metrics with driving behavior) are compared. The results show that for single time point prediction, MLP model has the highest accuracy among three machine learning models. The model solely based on EEG metrics datasets has higher accuracy than driving behavior as well as combined datasets. However, for multi-time point prediction, the accuracy of MLP is only 73.9%, worse than LR and RF. We improved the MLP model by adding attention mechanism layer and using random forest model to select important features. As a consequence, the accuracy is greatly improved and reaches 88%. This study demonstrates the importance and feasibility of EEG signals to identify unsafe drivers ahead. The improved neural network model can be helpful to reduce intersection accidents and improve traffic safety.

A multimodal approach for the ecological investigation of sustained attention: A pilot study

Natural fluctuations in sustained attention can lead to attentional failures in everyday tasks and even dangerous incidences. These fluctuations depend on personal factors, as well as task characteristics. So far, our understanding of sustained attention is partly due to the common usage of laboratory setups and tasks, and the complex interplay between behavior and brain activity. The focus of the current study was thus to test the feasibility of applying a single-channel wireless EEG to monitor patterns of sustained attention during a set of ecological tasks. An EEG marker of attention (BEI—Brain Engagement Index) was continuously recorded from 42 healthy volunteers during auditory and visual tasks from the Test of Everyday Attention (TEA) and Trail Making Test (TMT). We found a descending pattern of both performance and BEI in the auditory tasks as task complexity increases, while the increase in performance and decrease in BEI on the visual task. In addition, patterns of BEI in the complex tasks were used to detect outliers and the optimal range of attention through exploratory models. The current study supports the feasibility of combined electrophysiological and neurocognitive investigation of sustained attention in ecological tasks yielding unique insights on patterns of sustained attention as a function of task modality and task complexity.

Event-Related Potentials Analysis of the Effects of Discontinuous Short-Term Fine Motor Imagery on Motor Execution

In this study, event-related potentials and neurobehavioral measurements were used to investigate the effects of discontinuous short-term fine motor imagery (MI), a paradigm of finger sequential MI training interspersed with no-MI that occurs within 1 hr, on fine finger motor execution. The event-related potentials revealed that there were significant differences in the P300 between the fine MI training and the no-MI training. There were also significant changes in the P200 between fine motor execution of familiar tasks after MI training and fine motor execution of unfamiliar tasks without MI training. Neurobehavioral data revealed that the fine MI enhanced fine motor execution. These findings may suggest that discontinuous short-term fine MI could be useful in improving fine motor skills.

Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System

Physiological signals are immediate and sensitive to neurological changes resulting from the mental workload induced by various driving environments and are considered a quantifying tool for understanding the association between neurological outcomes and driving cognitive workloads. Neurological assessment, outside of a highly-equipped clinical setting, requires an ambulatory electroencephalography (EEG) headset. This study aimed to quantify neurological biomarkers during a resting state and two different scenarios of driving states in a virtual driving environment. We investigated the neurological responses of seventeen healthy male drivers. EEG data were measured in an initial resting state, city-roadways driving state, and expressway driving state using a portable EEG headset in a driving simulator. During the experiment, the participants drove while experiencing cognitive workloads due to various driving environments, such as road traffic conditions, lane changes of surrounding vehicles, the speed limit, etc. The power of the beta and gamma bands decreased, and the power of the delta waves, theta, and frontal theta asymmetry increased in the driving state relative to the resting state. Delta-alpha ratio (DAR) and delta-theta ratio (DTR) showed a strong correlation with a resting state, city-roadways driving state, and expressway driving state. Binary machine-learning (ML) classification models showed a near-perfect accuracy between the resting state and driving state. Moderate classification performances were observed between the resting state, city-roadways state, and expressway state in multi-class classification. An EEG-based neurological state prediction approach may be utilized in an advanced driver-assistance system (ADAS).

Event-Related Potential Sensing Analysis on the Risk Perception and Decision-Making by Grassroots Managers in Different Fatigue States

The risk perception and decision-making ability of grassroots managers is the key to the normal operation of enterprises. This study used event-related potential indicators (ERPs) to reveal the process of risk perception and decision-making behaviour of coal mine grassroots managers in different fatigue states. The ERP components, such as CNV, P300, MMN, and FRN, during risk perception, decision-making, and postperception periods were obtained and evaluated. The peak value and variation characteristics of ERP components of grassroots managers under fatigue and nonfatigue conditions were analysed. Accordingly, the effectiveness of decision-making behaviour in different periods was determined. The results showed that the P300 component is a key indicator in measurements of the deviation of grassroots managers' decision-making behaviour, and FRN could reflect the negative emotions in the decision-making process and reflect the sensitivity of the risk perception of grassroots managers. There was a significant difference between the peak voltages of the ERP components of the grassroots managers in fatigue and nonfatigue states. The peak voltage of the ERP components of the grassroots managers in a fatigue state was generally greater than 10 μV; therefore, the quality of decision-making by the grassroots managers could be evaluated according to the characteristics of the ERP components. This study provides a risk decision-making reference for grassroots managers of coal mine enterprises.

Vector Phase Analysis Approach for Sleep Stage Classification: A Functional Near-Infrared Spectroscopy-Based Passive Brain-Computer Interface

A passive brain-computer interface (BCI) based upon functional near-infrared spectroscopy (fNIRS) brain signals is used for earlier detection of human drowsiness during driving tasks. This BCI modality acquired hemodynamic signals of 13 healthy subjects from the right dorsolateral prefrontal cortex (DPFC) of the brain. Drowsiness activity is recorded using a continuous-wave fNIRS system and eight channels over the right DPFC. During the experiment, sleep-deprived subjects drove a vehicle in a driving simulator while their cerebral oxygen regulation (CORE) state was continuously measured. Vector phase analysis (VPA) was used as a classifier to detect drowsiness state along with sleep stage-based threshold criteria. Extensive training and testing with various feature sets and classifiers are done to justify the adaptation of threshold criteria for any subject without requiring recalibration. Three statistical features (mean oxyhemoglobin, signal peak, and the sum of peaks) along with six VPA features (trajectory slopes of VPA indices) were used. The average accuracies for the five classifiers are 90.9% for discriminant analysis, 92.5% for support vector machines, 92.3% for nearest neighbors, 92.4% for both decision trees, and ensembles over all subjects' data. Trajectory slopes of CORE vector magnitude and angle: m(|R|) and m(∠R) are the best-performing features, along with ensemble classifier with the highest accuracy of 95.3% and minimum computation time of 40 ms. The statistical significance of the results is validated with a p-value of less than 0.05. The proposed passive BCI scheme demonstrates a promising technique for online drowsiness detection using VPA along with sleep stage classification.

The Effects of Acute Exercise on Driving and Executive Functions in Healthy Older Adults

The benefits of exercise on cognitive functioning in older adults are well recognized. One limitation of the current literature is that researchers have almost exclusively relied on well-controlled laboratory tasks to assess cognition. Moreover, the effects of a single bout of aerobic exercise in older adults have received limited attention. The proposed study addresses these limitations by assessing the effects of a single bout of exercise on a more ecologically valid task - driving. Seventy-one participants (M _age = 66.39 ± 4.70 years) were randomly allocated to 20min cycling at moderate intensity or sitting and watching driving videos. Participants were then tested on their driving performance using a driving simulator. Driving performance was measured with three different scenarios assessing decision making, driving errors, reaction time, and attention. On a subsequent session, all participants were tested on executive functioning before and after a fitness test. Non-significant effects of exercise were observed on driving performance. However, participants performed better on the Trail Making Test (Cohen's d = 0.25) and Stroop test (d = 0.50) after the fitness test compared to their baseline. These results suggest that post-exercise cognitive improvements do not transfer to improved driving performance among healthy older adults. This study also highlights the importance of assessing expectations as a possible moderator of the effects of acute exercise on activities of daily living. Future studies must examine other relevant ecologically valid tasks and ensure similar expectations between experimental and control groups to further advance the knowledge base in the field.

Dynamic Changes in EEG Power Spectral Densities During NIH-Toolbox Flanker, Dimensional Change Card Sort Test and Episodic Memory Tests in Young Adults

Much is known about electroencephalograph (EEG) patterns during sleep, but until recently, it was difficult to study EEG patterns during conscious, awake behavior. Technological advances such as powerful wireless EEG systems have led to a renewed interest in EEG as a clinical and research tool for studying real-time changes in the brain. We report here the first normative study of EEG activity while healthy young adults completed a series of cognitive tests recently published by the National Institutes of Health Toolbox Cognitive Battery (NIH-TCB), a commonly-used standardized measure of cognition primarily used in clinical populations. In this preliminary study using a wireless EEG system, we examined power spectral density (PSD) in four EEG frequency bands. During baseline and cognitive testing, PSD activity for the lower frequency bands (theta and alpha) was greater, relative to the higher frequency bands (beta and gamma), suggesting participants were relaxed and mentally alert. Alpha, beta and gamma activity was increased during a memory test compared to two other, less demanding executive function tests. Gamma activity was also inversely correlated with performance on the memory test, consistent with the neural efficiency hypothesis which proposes that better cognitive performance may link with lower cortical energy consumption. In summary, our study suggests that cognitive performance is related to the dynamics of EEG activity in a normative young adult population.