## 3 Methodology
### Hyperparameters Used for Feature Selection Methods and Machine Learning  
Table 1 lists the hyperparameters used for the various feature selection methods in this study, detailing the specific values for SelectKBest, LASSO, and GA. It includes parameters such as scoring function, number of top features, regularization strength, population size, number of generations, crossover rate, and mutation rate. SelectKBest with an ANOVA _F_-test played a crucial role in the methodology aiming to identify the most informative features for emotion classification. This feature selection process involved ranking features based on the ANOVA _F_-value and selecting the top \(k\) features for further modeling. For the EEG Emotion datasets and DEAP datasets, we installed the SelectKBest method to choose 100, 500, 1000, 1500, and 2000 features and 50, 100, and 150 features, respectively. The varying number of features selected was chosen to systematically explore the impact of feature reduction on model performance, ensuring classification accuracy in the experiments for both the Emotion and DEAP datasets. This approach was necessary because filter-based feature selection requires the pre-determination of the number of selected features. Then, we applied hyperparameter optimization using BO for LASSO. Among the representative embedded-based feature selection methods, we employed LASSO, followed by hyperparameter optimization using BO. We optimized the 'alpha' value of LASSO using BO over a search space ranging from \(10^{-6}\) to \(10^{1}\) with a log-uniform distribution. We used 5-fold cross-validation to find the most predictive and robust 'alpha' value. A basic GA comprises three genetic operators: selection, mutation, and crossover. In GAs, a solution is typically represented as a binary string, called a chromosome. It is essential to evaluate and select the most effective solutions to a particular problem. Each solution is assigned a fitness value that reflects its proximity to the overall specifications of the desired solution. We used the accuracy of each machine learning model as the fitness value of wrapper-based feature selection using a GA. Selection: This operator examines a set of individuals in a population based on their fitness values. It preferentially retains the best individuals but must also provide the less fit individuals a chance to avoid premature convergence. We used a GA with population sizes of 30 and 20 generations and a 5-fold cross-validation. Mutation. This operation introduces a small perturbation in the chromosome of an individual, reflecting the operational characteristics of the proposed algorithm. We set the mutation probability to 0.8, 0.2, and 0.01 for the decay rate, respectively. Crossing. This explores the search space by diversifying the population. This operation typically manipulates the chromosomes of two parents to generate two children. These operations are applied iteratively in the GA, as shown in the flowchart (Figure 2). In our implementation, the crossover rate started at 0.2, increased exponentially, and approached 0.8 over 20 generations. These parameters were set to balance the exploration and exploitation capabilities of the GA during the feature selection process. Initially, a low crossover rate (0.2) encouraged further exploration of the solution. This helps in identifying diverse