# Information on Drivers' Sex Improves EEG-Based Drowsiness Detection Model
## 2 Methodology  
### Experimental Design  
In our study, 34 healthy participants were recorded during two sessions in a driving simulation. All participants had a valid driver's license and drove a car regularly but are not professional drivers. The EEG was recorded with a 32-channel actiCHAmp EEGamplifier (Brain Products, Munich, Germany) with passive sintered Ag/AgCl electrodes. The electrodes were located at prefrontal (Fp1, Fp2), frontal (F3, F4), central frontal (FC5, FC1, FC2, FC6), inferior frontal (F7, F8), midline frontal (Fz), central (C3, C4), midline central (Cz), midline parietal (Pz), central parietal (CP5, CP1, CP2, CP6), midline occipital (Oz), inferior temporal (T7, T8), posterior temporal (TP9, TP10), parietal (P3, P4), inferior parietal (P7, P8), posterior occipital (PO9, PO10), and occipital scalp sites (O1, O2). The electrodes were positioned according to the International 10-20 system guidelines. The electrode at the FCz position was used as a reference. A ground electrode was placed on the forehead. BrainVision Recorder software was used for impendence check and on-line monitoring. The EEG signals were recorded with a 1000 Hz sampling rate. A simulation scenario was shown on three wide LCD screens. The car was controlled with a professional steering wheel joystick and pedals. All participants were instructed to follow the traffic rules. Male participants averaged 30.24 of age with a standard deviation of 6.86 years and female participants averaged 30.12 years of age with a standard deviation of 6.98 years. The driving scenario was the same for all participants and consisted of driving on state roads and highways, and in an urban environment. The study was approved by the Ethics Committee of the University of Zagreb, Faculty of Electrical Engineering and Computing. Each participant had two recording sessions. The first recording session (alert session) began at 2:00 p.m. and the second one (drowsy session) began at midnight. The sessions were recorded on different days. The driving scenario was the same for both sessions: after 15 min of adaptation driving (simple acceleration, braking, and turning exercises), all participants were instructed to drive on a highway for 90 min, with adjusted day/night lighting according to the time of the session. Highway driving was monotonic, with very few other cars in traffic, and did not include any unexpected events (such as road crossings of animals or pedestrians). Room lighting, temperature, and humidity were controlled and were the same for both sessions. Each EEG recording was divided into 10 s epochs with five seconds' overlap between epochs. The first 250 epochs (-21 min) of highway driving in the alert session were labeled as periods with the highest alertness level for all participants. The last 250 epochs of the drowsy session (approximately after 70 min of highway driving) were labeled as periods with the highest drowsiness level for all participants. This labeling, where the beginning of the session is labeled as alert, and the end is labeled as drowsy, is commonly used in practice [27; 28; 29; 30]. These classifications of two-phase classes were additionally confirmed by an expert in psychophysiological behavior based on their self-assessment of drowsiness (Karolinska Sleepiness Scale) before and after the session, and visual inspection of participants. Participants indicated their drowsiness level on the Karolinska Sleepiness Scale for the alert session as extremely alert, very alert, alert, or fairly alert. Additionally, their total score on the Fatigue Assessment Scale indicated no fatigue before the alert session. Table 1 shows the number of male and female drivers in each class. Although we used a 32-channel EEG, we wanted to describe dependencies and differences between specific brain regions--front left (FL), front right (FR), occipital left (OL), and occipital right (OR) regions. We calculated the mean value for each feature from five channels in each region (see Table 2).