## 2 Background
Developing reliable methods for analyzing brain signals is difficult because they are complex, unstructured, and vary greatly depending on the person, their age, and their mental health. Furthermore, EEG recordings can be affected by extraneous noise from eye blinking and muscle movements. These artifacts can obscure the underlying neural activity, hindering the ability to accurately interpret the EEG data. Therefore, it is crucial to distinguish the relevant neural signals from the background noise and artifacts that occur during EEG recordings [13; 14]. To address this challenge, a robust and consistent EEG preprocessing method is essential. EEG data preprocessing encompasses a procedure to transform raw EEG data into a refined format by eliminating unwanted noise and artifacts, making it suitable for further analysis and user interpretation [15]. In the field of EEG signal classification, various methods have been proposed to enhance the accuracy and efficiency of the process. For instance, a method was proposed using pattern recognition and a Support Vector Machine (SVM) optimized by an Improved Squirrel Search Algorithm (ISSA) [16]. This method involves extracting time-domain features, applying an elliptical filter to isolate specific frequency bands, and utilizing a multichannel weighted Wiener filter for artifact reduction. Another approach was developed to anticipate individual dynamic ranges of EEG features, facilitating personalized calibration and improving overall performance [17]. These studies highlight the ongoing efforts to refine EEG signal-processing techniques for various applications. Additionally, EEG signals introduce complexities into the feature extraction and signal analysis process due to their non-stationary nature (with statistics change over time), non-linear behavior, and non-Gaussian distribution [18]. These unique properties must be considered and accommodated in the feature extraction process to establish a robust end-to-end pipeline. EEG signals can be categorized based on location, amplitude, frequency, morphology, continuity, synchrony, symmetry, and reactivity. The most common classification is based on frequency bands, with each band exhibiting unique characteristics and associations with various brain states. EEG patterns can be analyzed by dividing them into frequency ranges, with each range corresponding to specific patterns and brain activities. Commonly studied waveforms include delta (0.5-4 Hz), theta (4-7 Hz), alpha (8-12 Hz), sigma (12-16 Hz), and beta (13-30 Hz). Additional waveforms like infra-slow oscillations (ISO) and high-frequency oscillations (HFOs) have clinical relevance. EEG, which captures detailed neural electrical activity, provides valuable insights. The correlation between different brain regions proves crucial in depression detection, complementing the distinctive characteristics of individual channels. The growing interest in using EEG data for diagnosing depression has driven innovative research aimed at improving feature selection and classification approaches. Resting-state and sound-stimulated EEG data were analyzed in [19], where minimal-redundancy-maximal-relevance (mRMR) was employed for feature selection alongside the k-nearest neighbor classifier. In [20], L1-based, tree-based, and false discovery rate (FDR) feature selection methods were utilized along with various machine learning models, including SVM, KNN, decision tree, Naive Bayes, Random Forest, and Logistic Regression. Feature selection based on ranking features using the receiver operating characteristics (ROC) criterion in descending order was conducted in [21], followed by selecting subsets containing the top-ranked features. The classifiers used included Logistic Regression (LR), the Support Vector Machine (SVM), and Naive Bayesian (NB). The performance of the SVM on different EEG features was explored in [22].