## 4 Results and Analysis  
### Dataset  
We used the EEG Emotion dataset provided by [PERSON] et al. [9] and the DEAP dataset by [PERSON] et al. [39] for this study. The EEG Emotion dataset was collected from two people (1 male and 1 female) for 3 min per state--positive, neutral, and negative. They used a Muse EEG headband, which recorded the TP9, AF7, AF8, and TP10 EEG placements via dry electrodes. The Muse headband recorded data for sixty seconds with the streaming frequency within the range of 150-270 Hz. The sampling rate was reduced to 200 Hz using Fast Fourier transformations along an axis [40]. The features used in this study represent distinct frequency bands (e.g., delta, theta, alpha) extracted from EEG signals, which are well-established in the literature for their relevance in distinguishing emotional states. For the EEG dataset, [PERSON] et al. used the filtering method to extract meaningful features from the noisy raw signals, including Fast Fourier transform (FFT), Shannon entropy, and max-min feature extraction in temporal sequences using the filtering method [40]. The second dataset in this study was the DEAP dataset by [PERSON] et al. [39], which was recorded by 32 participants, each of whom watched 40 min-long extracts of music videos. The DEAP dataset includes 32-channel 512 Hz EEG data and peripheral physiological signals. The dataset was divided into four categories (valence, arousal, dominance, and liking). Participants rated arousal, valence, and dominance (integers between one and nine) using SAM mannequins on a discrete 9-point scale. Additionally, the participants rated the felt emotion using the emotion wheel. Data were recorded at two separate locations: participants 1-22 were recorded in Twente and 23-32 in Geneva. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Parameter** & **Explanation** & **Value** \\ \hline \multirow{6}{*}{RF} & n\_estimators & The number of trees & From 50 to 200 \\  & n\_estimators & in the forest & \\  & max\_depth & Maximum depth of tree & 10, 20, 30 \\  & min\_samples\_split & Minimum number of samples required to split & From 2 to 10 \\ \hline \multirow{6}{*}{Logistic Regression} & penalty & Specifies the norm of the penalty & 11, l2 \\  & & Inverse of & \\  & c & regularization strength & From 10\({}^{-3}\) to 10\({}^{3}\) \\  & solver & Optimization algorithm & Liblinear \\ \hline \multirow{6}{*}{XGBoost} & n\_estimators & Specifies the number of gradient-boosted trees & From 50 to 200 \\  & & Determine the percentage of improvement in learning rates & From 10\({}^{-3}\) to 1 \\  & max\_depth & Maximum tree depth for base learners & From 3 to 9 \\ \hline \multirow{6}{*}{SVM} & kernel & Sets the type of kernel that the algorithm uses to classify regularization parameter & linear, rbf \\  & c & parameter & From 10\({}^{-3}\) to 10\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameters of the classification algorithm used in this experiment. The data matrix of the DEAP dataset was 32 \(\times\) 40 \(\times\) 40 \(\times\) 8064, where 32 is the number of participants, 40 is the number of trials, 40 is the number of EEG channels, and 8064 is the experimental data based on video and sampling sequences. In this experiment, we used two labels (valence and arousal) for 22 participants whose frontal facial videos were recorded [39]. We extracted features using the band frequencies for each EEG channel corresponding to theta (4-8 Hz), low alpha (8-10 Hz), alpha (8-12 Hz), beta (12-30 Hz), and gamma (30-45 Hz). Finally, the data matrix was 880 \(\times\) 160, where 880 corresponded to 40 trials from 22 participants and 160 corresponded to five bands with 32 channels per band. Two labels (valence and arousal) were used in this study, as in [39], and high-pass filtered with a 2 Hz cutoff frequency using the EEGlab toolbox. This method removes slow, irrelevant signals, which are typically below 2 Hz.