## 2 Previous Works
They used five statistical methods, including logarithmic band power (LBP), standard deviation, variance, kurtosis, and Shannon entropy (SE), from DWT with level four decomposition, 'db4' wavelet, and EEG segments of length 50 s, to extract the features. They performed an extensive analysis of evaluating classification accuracies in both two- and three-class scenarios. They also conducted a study with three types of EEG datasets, considering both single-channel and multi-channel modes of diagnosis. They identified that SVM produced high classification accuracy, especially when combined with the logarithmic band power feature. In a different approach, [PERSON] et al. [31] proposed a method for analyzing EEG signals to classify various neurological disabilities, such as autism, epilepsy, Parkinson's disease, and schizophrenia, utilizing Convolutional Neural Networks (CNNs). As part of the pre-processing steps, EEG signals were segmented into 3 s intervals and transformedinto 2D time-frequency-spectrogram images using a short-time Fourier transform. The classification analysis, incorporating five-fold cross validation, aimed to distinguish neurological disorders from normal subjects. The results indicated that the proposed CNN model achieved a notable performance accuracy of 98.33% in classifying the disorders. Although numerous studies have been performed to identify neurological disorders or mental disorders by analyzing EEG data, the majority have centered on understanding individual disorders by simply applying diverse classification algorithms. The critical examination of each channel associated with neurological disabilities is essential for discerning both commonalities and distinctions among them. Despite this importance, limited studies have been performed to analyze multiple neurological disorders or mental disorders by evaluating their shared characteristics and variations. This study addresses this gap by undertaking a comparative analysis of various features, including EEG frequency bands, power spectral density, raw data, and wavelet features. Our study gauges the efficacy of these features in analyzing neurological disorders, utilizing a publicly available dataset containing diverse neurophysiological information encompassing conditions, such as ADHD, MDD, OCD, and Parkinson's disease. Multiple features were extracted from the dataset using various EEG segmentation techniques, including short-term EEG segmentation based on DWT. EG signals are irregular and non-stationary with changing magnitude values over time [31]. So, presenting information from a specific duration satisfying the stationarity assumption in EEG signals is important [32; 33]. Thus, the choice of a segmentation length in analyzing EEG signals is always challenging. Using longer segmentation lengths may produce misleading results [32] due to increased non-stationarity. Also, utilizing longer segmentation lengths typically results in an elevated computational cost [34; 35]. Numerous studies are conducted on a single neurological disorder with various segmentation lengths empirically as 1 s, 2 s, 3 s, and 5 s [16; 31; 35; 36; 37; 38]. In this study, we examined two segmentation lengths (1 s and 2 s) to identify neurological disabilities by comparing their features, classification performance results, and visual representations. To the best of our knowledge, no previous studies have provided detailed comparisons illustrating feature extraction, classification performance results, and visual representation in different segmentation lengths with various neurological disorders. To advance our understanding of the distinctions among neurological disorders, our study advocates adopting a sophisticated analysis technique--visual analytics. In detail, multiple visualization methods were employed to generate graphical representations of data, facilitating interactive visual analysis for users.