## 1 Introduction  
A brain-computer interface (BCI) is a communication system that does not require peripheral muscle activity, allowing subjects to enable the transmission of commands to electronic devices through brain activity [1, 2]. These systems are brain systems that provide a new communication channel for patients with movement disorders such as infarcts or amyotrophic lateral sclerosis. BCIs can be classified into invasive and non-invasive types based on how the brain signals are recorded: invasive BCIs involve surgically implanted electrodes directly into the brain tissue, while noninvasive BCIs use external sensors to detect brain activity through the scalp. An invasive BCI provides better-quality data than a noninvasive BCI because it is inserted into the brain to read signals. It has the advantage of measuring brain signals [3]. A noninvasive BCI is not placed inside the brain. It also displays the EEG signals, which are the electrical signals of the brain. Therefore, it has the advantage that the general public can easily use it [4]. The field of emotion recognition has gained significant attention in recent years due to its potential applications in various domains, such as human-computer interaction, mental health monitoring, and affective computing. Emotion recognition from electroencephalogram (EEG) brainwave signals has emerged as a promising approach for understanding and analyzing human emotions. EEG-based emotion recognition involves extracting specific features from EEG signals, followed by the use of classification algorithms to accurately categorize emotions. Research analyzing EEG data using machine learning or deep learning technology has progressed over the past decade. These studies have also differentiated dementia patients from healthy people [5,6]. Several studies have explored the feasibility and effectiveness of using EEG signals to decipher human emotions [7]. In this section, we review some key related work examples that contribute to our understanding of EEG-based emotion recognition. We can now include some studies that have achieved better accuracy than the earliest studies. According to [PERSON] et al. [8], the EEG dataset used in their research was obtained from recurrent neural networks, long short-term memory (LSTM), and gated recurrent (GRU) networks. [PERSON] et al. [9,10] classified emotions using a convolutional neural network and various feature selection and ensemble models to classify emotions. [PERSON] and [PERSON] [11] focused on recent advancements in remote wearable neuroimaging devices, particularly EEG, for health monitoring. Their study explored the integration of EEG with other modalities such as fNIRS and PPG for comprehensive emotion detection [11]. [PERSON] et al. [12] investigated resting-state functional (FC) within major brain networks in Parkinson's disease patients using EEG. [PERSON] and [PERSON] [13] applied statistical feature selection methods to the DEAP dataset and classified them using two labels (valence and arousal). Our objective is to enhance classification performance through optimal feature selection and machine learning models. In this study, we developed an accurate emotion classification using EEG brainwave signals. Our method enhances emotion classification by applying various feature selection methods and optimizing machine learning models using Bayesian optimization (BO). We conducted experiments using three feature selection methods and four machine learning algorithms. The main contributions of this paper are summarized as follows:  
* We evaluated the performance of several machine learning algorithms, including random forest, logistic regression, XGBoost, and SVM, to analyze the machine learning performance with feature selection for EEG-based emotion classification. Additionally, we tuned the hyperparameters of the machine learning models using Bayesian optimization (BO).