# Information on Drivers' Sex Improves EEG-Based Drowsiness Detection Model
## 4 Discussion  
Splitting the dataset into two subsets, male and female, resulted in a significant improvement in drowsiness detection (confirmed with Mann-Whitney U test). After adding sex as a feature to the dataset, drowsiness detection was only incrementally improved, but splitting into two sex-dependent datasets yielded a 3% and 7% improvement for male and female drivers, respectively. Precision and recall were higher for alert and drowsy states for both male and female subsets. Interestingly, precision and recall were higher for female drivers than for male drivers. This increase in prediction accuracy, precision, and recall for female drivers suggests differential changes in EEG activity associated with drowsiness compared to male drivers. Theoretically, approaches using sex as a feature and manual split of the dataset could have the same accuracy. The reason why this is not the case could be the relatively high dimensionality of the dataset and the relatively small number of examples in the dataset. For this reason, the algorithm was unable to optimize the hyperplane to artificially separate the data based on the sex feature. A recent review of EEG signal features and their application in driver drowsiness detection systems summarized 39 papers, none of which used participant's sex as a feature [13]. Based on our results, it is reasonable to assume that the model accuracy presented in many of these papers could be further increased by using sex as a feature or by splitting the dataset into male and female subsets. For these results to be applicable to all datasets, a high-accuracy sex classifier is needed. The average classification accuracy of the segment's sex (blue markers in Tables 5 and 6) is 93% and 92% for alert and drowsy drivers, respectively. On average, the classifiers correctly classify 97% of the alert participants and 96% of the drowsy participants (green markers in Tables 5 and 6). When majority voting is applied to each participant across all five runs, the accuracy of both classifiers is 100%. Other studies with high accuracy of sex classification models based on EEG were those of [PERSON] et al. [38] and [PERSON] et al. [39]. Their classification accuracy was 96.7% and 97.5%, respectively. The experimental design was the same in both papers, participants were measured in a relaxed resting position with their eyes closed. In both works, the discrete wavelet transform was used to obtain the frequency-domain features. Their final classifiers were based only on these frequency-domain features. In comparison, our participants were measured while driving, which is a complex mental activity. [PERSON] et al. achieved their accuracy with the usage of LOSOCV on the 60 participants. Their high accuracy suggests that a higher number of participants could increase our LOSOCV closer to the segments split based accuracy. Our previous work showed that frequency-domain features and RQA features differ significantly between alert male and female drivers [26]. Similar statistical results were found when brain activity was analyzed between male and female drowsy drivers. Female drowsy drivers showed significantly higher relative beta power in all regions and significantly higher relative alpha power in all regions except the Cz electrode. Male drowsy drivers showed significantly higher relative delta power in all regions and relative theta power in the occipital region. On the other hand, relative gamma power showed a different pattern between male and female drowsy drivers. In female drowsy drivers, relative gamma power was significantly higher in the occipital region, whereas in male drowsy drivers, relative gamma power was higher in the frontal region. Furthermore, the results of this paper showed that the features in the frequency domain are more correlated in males than in females during both alert and drowsy sessions.