## 4 Results and Analysis
### Exploratory Data Analysis  
We used two EEG datasets (EEG Emotion and DEAP) for the experiments in this paper. In the previous study [42], [PERSON] et al. generated an EEG Emotion dataset containing 2548 features. These features were created using various statistical methods, such as FFT for each time window, log entropy for every 0.5 s, Shannon entropy for every 1 s, and the minimum and maximum values for each window. Derivatives of the minimum and maximum values were calculated by dividing the window time and quarter-second min, max, and mean derivatives. For the DEAP dataset, we extracted features such as delta, theta, alpha, beta, and gamma bands from the raw EEG data, which represent different states of brain activity. As mentioned in Section 4.1, the DEAP data matrix was 880 \(\times\) 160 after filtering for the five frequency bands. We analyzed the distributions among features, and the results showed a balanced distribution, with outliers identified in approximately 34.5% of features for the EEG Emotion dataset. These outliers may reflect noise inherent in EEG recordings. An analysis of the label distribution shows that each label exhibits a similar distribution. Additionally, this balance ensures that the data can provide unbiased insights across different emotional states during the classification process. For the DEAP dataset, the distribution of valence and arousal labels shows nearly equal representation for each class. This balance indicates that the DEAP dataset offers impartial insights across various emotional categories during the classification process. Each feature in the DEAP dataset contained an average of 3.25% of the outliers. These outliers may require further investigation or removal to ensure the robustness and accuracy of the classification results. Due to the large number of features in the two EEG datasets, comprising 2548 and 160 features, respectively, explaining each feature in detail within the paper is challenging. Therefore, some analyses were conducted for the part of a whole feature set. Figure 3 illustrates violin plots for representative statistical features extracted from the EEG Emotion dataset. The plots show the distribution of these statistical features, capturing variations in the data. Each plot reflects the density of the data across a range of values, with some features showing high variability (like entropy in Figure 3m), while others (like standard deviation in Figure 3i) exhibit less variability. Figure 3: Violin plots of statistical features in the EEG Emotion dataset: (**a**) mean, (**b**) mean difference (computed between windows), (**c**) min, (**d**) min difference (computed between windows), (**e**) min difference (computed for each quarter window), (**f**) max, (**g**) max difference (computed between windows), (**h**) max difference (computed for each quarter window), (**i**) standard deviation, (**j**) standard deviation difference (computed between windows), (**k**) log, (**l**) correlation, (**m**) entropy, (**n**) FFT. Figure 4 depicts violin plots for ten randomly selected features in the DEAP dataset. The shape of these violins reveals the concentration and variability of the data for each feature. The narrow distributions suggest that most of the feature values are concentrated around central values, whereas features with broader distributions indicate greater variability. These features are crucial in analyzing and modeling from the DEAP dataset. Figures 3 and 4 provide a detailed view of how the data are distributed, showing both the density of the data at different values and the central tendencies, such as the variance, density, and quartiles. These analyses reveal that some features with similar distributions exhibit analogous patterns, indicating that redundancy in the datasets can lead to inefficiencies in the model's performance and increase unnecessary computations. Therefore, feature engineering, such as feature selection, becomes essential.