## 2 Previous Works
They assessed classification performance using various ML classifiers, including DT, KNN, SVM, ensemble classifiers, Naive Bayes, linear discriminant analysis (LDA), and LR, and neural network (shallow and deep) models. They segmented ten seconds of non-overlapping windows to extract features from EEG data. In detail, they used discrete wavelet transform (DWT) to decompose EEG data into different frequency levels with diverse EEG frequency bands (i.e., \(\alpha,\beta,\delta,\theta\)) to extract features such as mean, standard deviation, median absolute deviation, variance, interquartile range, kurtosis, and skewness. They showed the effectiveness of DWT in analyzing EEG data by evaluating the performance differences of multiple predictive models using combined features from nineteen channels. [PERSON] et al. [24] presented a method for diagnosing MDD by utilizing EEG power features with various machine learning algorithms, such as SVM, Naive Bayes (NB), and logistic regression (LR). They conducted data standardization through z-scores to identify critical features by eliminating potential outliers. The study revealed that SVM yielded high classification results in the analysis of MDD. [PERSON] et al. [25] used data collected from multiple academic institutions (i.e., the University of Turku, the University of New Mexico, and the University of Iowa) to investigate [PERSON]'s disease. Sample entropy was used to extract features from different EEG frequency bands, delta ([0.5-4] Hz), theta ([4-8] Hz), low alpha ([8-10] Hz), high alpha ([10-13] Hz), and beta ([13-30] Hz). They developed a greedy-based feature selection procedure to determine the optimal channels for disease analysis. The relevance of the channel was determined by measuring the area under the ROC curve. Consequently, the performance using one channel and five channels with the eyes open (EO) datasets was determined as 67% and 76%, respectively. Additionally, crucial channels were identified as frontal, left-temporal, and midline-occipital. [PERSON] et al. [26] proposed an approach for dyslexia detection using principal component analysis (PCA). To address the data imbalance problem, they applied synthetic minority oversampling techniques (SMOTEs). Then, classification performance results to detect dyslexia were evaluated using various machine learning algorithms, including SVM, LR, RF, NB, decision trees, and KNN. The results indicated that the combination of SMOTE and RF accurately identified dyslexia. In a comprehensive review, [PERSON] et al. [27] explored diverse approaches utilizing machine learning algorithms for dyslexia identification based on EEG signals. Their findings suggested that SVM outperformed other machine learning algorithms in this context. [PERSON] et al. [28] concentrated on categorizing chronic pain into three groups (high, moderate, and low) by employing approximate entropy (ApEn) and absolute band power from five frequency bands: delta ([0.1-4] Hz), theta ([4-8] Hz), alpha ([8-12] Hz), beta ([12-30] Hz), and gamma ([30-100] Hz). By analyzing multiple datasets with chronic neuropathic pain, they found that combining three bands (theta, alpha, and beta) with the ApEn feature separated the pain group well with the approximate entropy. [PERSON] et al. [29] performed a classification of patients with obsessive-compulsive disorder (OCD) using SVM with EEG segments of 2 s. By employing three entropy measurements (approximate entropy, sample entropy, and permutation entropy) after segmenting EEG in 2 s, they determined that permutation entropy yielded the highest accuracy in classifying OCD. Researchers have also performed various multi-class classification studies for analyzing neurological disorders with EEG data. [PERSON] et al. [30] conducted a study to analyze neurological disorders by extracting features from EEG sub-bands and applying multiple classification algorithms, such as LDA, SVM, KNN, and ANN.