## 2 Materials and Methods  
### DEAP Dataset and Pre-Processing  
This study was performed on the dataset DEAP developed by researchers at Queen Mary University of London [27]. This dataset is publicly available ([[http://www.eecs.qmul.ac.uk/mmv/datasets/deap/index.html](http://www.eecs.qmul.ac.uk/mmv/datasets/deap/index.html)]([http://www.eecs.qmul.ac.uk/mmv/datasets/deap/index.html](http://www.eecs.qmul.ac.uk/mmv/datasets/deap/index.html))) and consists of multimodal physiological signals for human emotion analysis. It contains, in total, 32 EEG-channel recordings and eight peripheral signals of 32 subjects (50 percent females, aged between 19 and 37). The carefully selected 40 1-min videos were used as emotion elicitation materials [27]. As shown in Figure 1, the 2D valence-arousal emotion model by [PERSON] [15] was used to quantitatively describe emotional states. The first dimension, valence, ranges from unpleasant to pleasant, and the second dimension, arousal, changes from bored to excited. Therefore, the valence-arousal model can describe most variations in human emotion changes. The well-known self-assessment manikins (SAM) [49] (shown in Figure 2) were adopted for self-assessment along the valence and arousal dimensions, and the corresponding discrete rating values change from 1 to 9, which can be used as identification labels in emotion analysis tasks [27]. In this paper, the first 32-channel EEG records (marker in Figure 3) in the DEAP dataset preprocessed in MATLAB format were used. The EEG signals were preprocessed by down-sampling from 512 Hz to 128 Hz, and then band-pass filtering with 4-45 Hz.  
Figure 1: 2D valence-arousal emotion model by [PERSON].  
In this work, two different binary classification problems were posed for subject-dependent emotion recognition: The discrimination of low/high valence (LV/HV), and low/high arousal (LA/HA). The subjects' ratings (scaling from 1 to 9) by SAM in the experiments [27] were used as the ground truth and the threshold was selected as 5 to divide the rating values into two categories: LV/HV and LA/HA. The time duration of one trail for each subject in the preprocessed EEG sequences is 63 s, in which the first 3 s are baseline signals before watching video elicitations. The 3 s sequences were removed to obtain the stimulus-related dynamics. The remaining 60-s EEG signals (thus, 7680 readings in each EEG channel, in total) were segmented into sixty 1 s epochs. Thus, there were 40 * 60 EEG epochs, in total, for each participant. Each subject-dependent EEG data had a dimensionality of 128 (sampling points) * 32 (EEG channels) * 2400 (EEG epochs). Finally, we obtained the labeled EEG signals with the dimension of 2400 for each subject. In this paper, for each subject, 10% of labeled epochs were used to train the emotion classifier, and the remaining 90% for test. For example, the constructed EEG dataset for the first participant consisted of 960 LV and 1440 HV epochs. Then, 10% of samples were randomly selected from LV and HV samples, respectively, and 240 epochs were selected for training.  
Figure 3: International 10-20 system for 32 electrodes (marked with blue circles).  
Figure 2: Images used for self-assessment manikins (SAM): (**a**) Valence SAM, (**b**) arousal SAM.  
Ten-fold cross-validation was used to evaluate the introduced LORSAL classifier, and the compared traditional methods.