An Efficient Approach for Driver Drowsiness Detection at Moderate Drowsiness Level Based on Electroencephalography Signal and Vehicle Dynamics Data

Background

Drowsy driving is one of the leading causes of severe accidents worldwide. In this study, an analyzing method based on drowsiness level proposed to detect drowsiness through electroencephalography (EEG) measurements and vehicle dynamics data.

Methods

A driving simulator was used to collect brain data in the alert and drowsy states. The tests were conducted on 19 healthy men. Brain signals from the parietal, occipital, and central parts were recorded. Observer Ratings of Drowsiness (ORD) were used for the drowsiness stages assessment. This study used an innovative method, analyzing drowsiness EEG data were in respect to ORD instead of time. Thirteen features of EEG signal were extracted, then through Neighborhood Component Analysis, a feature selection method, 5 features including mean, standard deviation, kurtosis, energy, and entropy are selected. Six classification methods including K-nearest neighbors (KNN), Regression Tree, Classification Tree, Naive Bayes, Support vector machines Regression, and Ensemble Regression are employed. Besides, the lateral position and steering angle as a vehicle dynamic data were used to detect drowsiness, and the results were compared with classification result based on EEG data.

Results

According to the results of classifying EEG data, classification tree and ensemble regression classifiers detected over 87.55% and 87.48% of drowsiness at the moderate level, respectively. Furthermore, the classification results demonstrate that if only the single-channel P4 is used, higher performance can achieve than using data of all the channels (C3, C4, P3, P4, O1, O2). Classification tree classifier and regression classifiers showed 91.31% and 91.12% performance with data from single-channel P4. The best classification results based on vehicle dynamic data were 75.11 through KNN classifier.

Conclusion

According to this study, driver drowsiness could be detected at the moderate drowsiness level based on features extracted from a single-channel P4 data.

A novel convolutional neural network method for subject-independent driver drowsiness detection based on single-channel data and EEG alpha spindles

A significant number of fatal accidents are caused by drowsy drivers worldwide. Driver drowsiness detection based on electroencephalography (EEG) signals has high accuracy and is known as a reference method for evaluating drowsiness. Among brain waves, EEG alpha spindle activity is a silent feature of decreasing alertness levels. In this paper, based on the detection of EEG alpha spindles, a novel driver drowsiness detection method is presented. The EEG spindles were detected using Continuous Wavelet Transform (CWT) analysis and the Morlet function. To do so, the signal is divided into 30-s epochs, and the observer rating of drowsiness determines the drowsiness level in each epoch. Tests were conducted on 17 healthy males in a driving simulator with a monotonous driving scenario. The Convolutional Neural Network (CNN) is used for classifying EEG signals and automatically learns features of the early drowsy state. The subject-independent classification results for single-channel P4 show 94% accuracy.

"I have strong medicine, you see": Strategic responses to racial microaggressions

In this qualitative study, the authors examined responses to racial microaggressions among community members in Montreal, Canada. To this end, using interpretative phenomenological analysis (IPA; Smith, Flowers, & Larkin, 2009) we conducted individual interviews with Black Canadian (n = 5) and Indigenous (n = 5) community members who pursued employment directly after secondary education. Seven themes emerged from the data (e.g., calling out perpetrators, empowering self and others, choosing to not engage, and using humor). Response strategies convey 4 primary features: (a) importance of social support in accessing resources and confronting racial microaggressions, (b) use of culturally grounded strategies as a form of resistance, (c) multifaceted use of humor to confront and to minimize racial microaggressions, and (d) intentional use of avoidance among women participants. Implications and directions for future research are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Efficacy and safety of Cerebrolysin treatment in early recovery after acute ischemic stroke: a randomized, placebo-controlled, double-blinded, multicenter clinical trial

Background and Purpose : The aim of this study was to evaluate the efficacy, safety, and tolerability of cerebrolysin in the early recovery phase after acute ischemic stroke. Methods. This prospective, randomized, double-blinded, placebo-controlled, multicenter, parallel-group study enrolled a total of 100 patients within 18 h after the onset of stroke. The patients were treated with Cerebrolysin (30 mL over seven days followed by 10 mL until day 30) or placebo once daily over a period of four weeks. Efficacy was primarily assessed by the NIH Stroke Scale at day 30, and additional parameters included the modified Rankin Scale, the Clinical Global Impression, the Patient Global Satisfaction (PGS) and the Mini Mental State Examination (MMSE). Nonparametric statistical procedures employing the Wilcoxon-Mann-Whitney test were used for data analysis. Safety and tolerability were assessed by adverse events, vital signs, and laboratory parameters. Results.The estimated effect size on the change from baseline in the NIH Stroke Scale on day 30 indicated a medium to large superiority of cerebrolysin compared to placebo (Mann-Whitney [MW] 0.66; 95% confidence interval [CI] 0.55-0.78, P=0.005). Similar effect sizes were reported for the modified Ranking Scale (MW 0.65; 95% CI 0.54-0.76; P=0.010) and the Clinical Global Impression (MW 0.70; 95% CI 0.55-0.85; P=0.006). Effect sizes in the MMSE and PGS did not reach statistical significance. No significant group differences were seen in any of the safety parameters. Conclusions. Cerebrolysin was effective, safe, and well tolerated in the early recovery phase after acute ischemic stroke and significantly improved neurological and global function outcomes compared to placebo.

Chromosomal location of the cadmium uptake gene (Cdu1) in durum wheat

Levels of the heavy metal cadmium (Cd) in food products are a food safety concern. Grain Cd is higher in durum (Triticum turgidum L. var. durum) than in common wheat, so reduction of Cd in durum grain is a priority of breeding programs. Previous research demonstrated that a single dominant gene, Cdu1, confers the low grain Cd phenotype, but the map location of the gene is not known. A doubled haploid population segregating for Cd concentration, developed from the cross of W9262-260D3 (a Kyle*2/Biodur inbred selection with low Cd uptake) and Kofa (high Cd uptake) and mapped with microsatellite markers, was used to locate Cdu1. Grain Cd concentration was determined by standard laboratory methods on field grain samples in 2000 and 2001. The Cd concentration segregated bimodally, allowing Cdu1 to be mapped qualitatively as well as quantitatively with quantitative trait locus analysis. The Cdu1 gene mapped to the long arm of chromosome 5B.