# Information on Drivers' Sex Improves EEG-Based Drowsiness Detection Model
###### Abstract  
Objective detection of a driver's drowsiness is important for improving driving safety, and the most prominent indicator of drowsiness is changes in electroencephalographic (EEG) activity. Despite extensively documented behavioral differences between male and female drivers, previous studies have not differentiated drowsiness detection models based on drivers' sex. Therefore, the overall aim of this study is to demonstrate that drowsiness detection can be improved with the use of drivers' sex information, either as a feature or as separate sex-dependent datasets. Additionally, we aim to provide a reliable EEG-based sex classification model. The used dataset consists of 17 male and 17 female drivers which were evaluated during alert and drowsy sessions. Frequency-domain and recurrence quantification analysis EEG features were used. Four classification algorithms and three feature selection methods were applied to build the models. The accuracy of drowsiness detection based on sex-dependent datasets is 84% for male drivers and 88% for female drivers, which is 3% and 7% better, respectively, than the classification without information about driver's sex (81%). The model for sex classification based on EEG achieved high accuracy: 97% correctly identified participants in alert sessions and 96% in drowsy sessions. All participants were correctly classified after the application of majority voting on five algorithm runs. The results suggest that sex-dependent datasets improve the accuracy of drowsiness models, which may be relevant to a variety of drowsiness detection systems currently being developed in the field. drowsiness detection; EEG features; machine learning; recurrence quantification analysis; sex classification; sex differences +
Footnote †: journal: Medical  
on electroencephalogram (EEG), electrocardiogram (ECG), respiration, electrooculogram, electromyogram, galvanic skin response, or skin temperature [7]. ECG records the heart's electrical activity (heartbeats), while heart rate variability (HRV) refers to the variations in times between two adjacent heartbeats. HRV is considered a good drowsiness predictor and accuracies of drowsiness detection based on HRV range from 56.6% to 95% among studies [8]. Respiration can be derived from HRV but is also measured using several other techniques. [PERSON] et al. [9], in their recent research, used radar for non-invasive respiration measurement and obtained 87% drowsiness classification accuracy. Measuring skin temperature can also be used for the assessment of drowsiness. [PERSON] and [PERSON] [10] measured temperature on the nose and wrist, and obtained accuracies of 68.4% and 88.9%, respectively. Electrooculogram measures eye movement characteristics. It is often used for drowsiness detection, achieving accuracies that range from 64% to 99% among studies [11]. EEG provides a very accurate assessment of the driver's mental state [12], achieving accuracies of drowsiness detection that range from 67% to 99% among studies [13]. Furthermore, changes in EEG activity are considered biomarkers of mental fatigue and drowsiness [14]. The most prominent indicator associated with mental fatigue and drowsiness is increased theta activity in frontal, central, and posterior cortical sites. In addition, increased alpha activity is associated with individual variability in cortical changes related to mental fatigue and drowsiness. Many studies also use ratio indices between these frequency bands as indicators of drowsiness [15], and a recent study suggests that multichannel ratio indices could bring an additional increase in drowsiness detection accuracy [16]. However, the results obtained are from studies with male participants or males and females as a group, without separating them by sex. Differences in driving behavior between men and women have been extensively documented using experimental driving tasks, attitudes toward driving, behavioral analysis, risk perception, and the number of accidents.