## 3 Methods
### Feature Extraction and Selection  
Data pre-processing plays a crucial role in eliminating noise and obtaining clean data for EEG analysis. In this study, we employed notch and band-pass filters to enhance the data quality. Since the EEG data contain a 50 Hz frequency associated with power line noise, the notch filter was applied for frequency removal. Then, the band-pass filter within the frequency range of 0.5 to 100 Hz was employed for further refinement. Feature extraction is an essential step influencing the overall classification performance. To analyze the EEG data, a non-overlapping segmentation is utilized to split EEG signals into distinct segments. For the multi-channel EEG data of subject \(k\in\{1,2,\cdots,N\}\), where \(N\) indicates the total number of the subjects, including healthy and unhealthy subjects, each channel is divided into segments with a pre-defined segmentation length. Specifically, \(\mathcal{D}^{i}=\{X_{1},X_{2},\cdots,X_{N_{k}}\}\), where \(N_{k}=\left\lceil\frac{l}{s}\right\rceil\) represents the total number of segments for the subject \(k\), and \(l\) denotes the total length of the EEG data. The \(i\)-th segment of a channel is represented as \(\mathcal{C}^{i}\) containing \(T\) instances, where \(1\leq i\leq N_{k}\) and \(T=m_{l}\times f_{s}\), where \(m_{l}\) is the pre-defined segmentation length and \(f_{s}\) is a sampling rate. As the suitable segmentation length for EEG data analysis is unknown, we evaluated the EEG data with two segmentation lengths (i.e., \(m_{l}=1\) second, 2 seconds). The class label of the \(i\)-th channel is determined by \(\mathcal{C}^{i}\in\{0,1\}\), and 0 and 1 represent \"normal\" and \"abnormal\", respectively. \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Male** & **Female** \\ \cline{2-3}  & **\# of Subjects (Mean \(\pm\) SD)** & **\# of Subjects (Mean \(\pm\) SD)** \\ \hline MDD & 67 (42.11 \(\pm\) 12.48) & 66 (43.59 \(\pm\)13.53) \\ \hline Dyslexia & 12 (11.04 \(\pm\) 2.17) & 9 (9.68 \(\pm\) 0.92) \\ \hline Chronic pain & 3 (34.95 \(\pm\) 15.29) & 9 (49.95 \(\pm\) 4.40) \\ \hline OCD & 10 (36.33 \(\pm\) 13.94) & 12 (36.15 \(\pm\) 14.63) \\ \hline ADHD & 8 (29.96 \(\pm\) 14.14) & 26 (20.68 \(\pm\) 13.20) \\ \hline Parkinson & 4 (66.46 \(\pm\) 11.01) & 13 (65.86 \(\pm\) 9.04) \\ \hline Burnout & 1 (-) * & 5 (50.02 \(\pm\) 2.33) \\ \hline Healthy & 32 (23.41\(\pm\) 19.36) & 16 (31.19\(\pm\) 15.48) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptive statistics for the data used in this study. (* no age information is found in the original dataset. )We employed four different feature extraction approaches to extract features from each segment to compare and examine the capability of identifying the abnormal condition. The extracted features encompass raw features, EEG frequency band information, power spectral density (PSD), and wavelet features. The raw features represent the features extracted directly from each segment, calculated by determining the average of the z-score, mean, variance, kurtosis, and entropy. The EEG frequency band information is broadly used in analyzing EEG data. To extract EEG frequency band features, commonly used EEG frequency band information, such as \(\alpha,\theta,\gamma,\beta\), and \(\delta\), was identified in each segment. Then, the average and standard deviation of the frequency band were computed. For the extraction of PSD features, [PERSON]'s periodogram [40] was employed to compute the frequency spectrum of EEG signals using fast Fourier transform (FFT). The resulting PSD features comprised five attributes: frequency, energy, variance, sample entropy, and permutation entropy. While FFT is effective in identifying the underlying frequency information within the EEG data, it struggles to detect local transient changes at specific frequencies. Therefore, wavelet transform (WT) is considered due to its suitability for analyzing non-stationary data, offering the capability to extract both frequency (scales) and time information.