## 3 Experiment and Results
### Evaluation Datasets  
The performance of the proposed stress recognition method was evaluated using three datasets obtained from EEG signals generated by audio-visual stimuli.  
* The database for emotion analysis using physiological signals (DEAP) [30] is a publicly available dataset for emotional classification. It comprises multiple physiological signals collected from 32 participants, aged between 19 and 37 years, who watched 40 music videos for 60 s each. Among the different signals, there were EEG signals collected at a sample rate of 512 Hz from participants using 32 channels/electrodes, which were positioned according to the international 10-20 system. The collected EEG signals were downsampled to 128 Hz; electrooculogram (EOG) artefacts and noises were preprocessed; the data were then averaged as a common reference. The self-assessment manikin (SAM) scale, provided by DEAP for measuring the valence and arousal based on [PERSON]'s model for emotional representation, was included for each music video. The valance, arousal, dominance, and preference were scored in 4 dimensions, each ranging from 1 to 10. The emotional state was labeled based on the arousal and valence of the SAM scale. Low arousal and high valence are considered to constitute a state of calm. Conversely, a stress state is when the valence is low and the arousal is high. A total of twenty-four participants from this dataset were screened and their annotated valence and arousal values were applied to (14) and (15) [31] to classify the EEG signals into two state classes, those of calm and stress. \[\text{Calm}=(\text{arousal}<4)\cap(4<\text{valence}<6),\] (14) \[\text{Stress}=(\text{arousal}>5)\cap(\text{valence}<3).\] (15)
* The virtual reality environment (VRE) dataset was obtained from a climbing virtual reality system, comprising periods of stressful climbing over rugged mountain and periods of rest (or calm) between the climbs. The virtual reality system was designed to enable participants to receive and respond to audiovisual feedback through an Oculus Rift, which included EEG caps. Twelve participants, including 6 males and 6 females aged between 20 and 30 years, wore an Oculus Rift with built-in displays and lenses and experienced the VRE, and EEG signals were recorded for 10 min from each participant at a sampling rate of 512 Hz using an EEG cap with 32 channels/electrodes.  
* The EEG dataset for the emotional stress recognition (EDESC) is a dataset containing EEG signals obtained from 20 participants, including 10 males and 10 females aged between 18 and 30 years. The EDESC recorded data at a sampling rate of 256 Hz in two stages, before and after an activity, using a four-channel EEG headband. In the preactivity phase, EEG data were collected for 3 min from participants sitting in a comfortable position in a quiet room with their eyes open. In the postactivity phase, EEG data were collected for 3 min from the participants sitting in the measurement room after they performed the activity. The perceived stress scale (PSS) questionnaire was used to classify the EEG signals as stressed or nonstressed. If the PSS score was 20 or higher, the subject was classified as stressed, whereas the subject was classified as nonstressed if the score was less than 20. The authors who created the database compared the pre- and postactivity phases, and reported that the preactivity phase was more accurate for identifying stress. Therefore, we applied the preactivity phase data to our experiments by dividing them into two classes (stressed and nonstressed) and three classes (stress-free, mild, and stressed).