## 6 Conclusions and Future Works  
This study presents a novel approach to analyzing EEG data with neurological disabilities, incorporating DWT, ML classification algorithms, and visualization techniques. Despite various existing methods utilizing feature extraction and machine learning, previous studies have faced challenges in accurately identifying disabilities and enhancing understanding of the features and data. To address these limitations, we introduced a feature extraction approach using DWT to differentiate disabilities with short-length segmentations (e.g., 1 s and 2 s). We evaluated the effectiveness of this approach by employing multiple feature extraction methods (extracting the EEG frequency band, power spectral density, and raw features) and comparing their classification performance with wavelet features. Specifically, we conducted single-channel-based and region-based classifications. Then, further extensive analysis was performed through visual analysis using multiple visualization techniques. The comprehensive analysis revealed that wavelet features with the region-based classification exhibited strong performance results in differentiating disabilities, with the frontal brain region proving particularly effective. Among the classification algorithms, RF and KNN presented good performance results in the region-based classification, with RF demonstrating higher performance than other algorithms such as SVM, KNN, and LR. While evaluating different decomposition levels and wavelets for analyzing disabilities, no explicit patterns were observed. However, we noted that the 'db4' wavelet with 2 s segmentation and level six decomposition showed high classification performance results in the single-channel-based classification. Additionally, we found that the 'coif' wavelet yielded high performance with level seven decomposition, and the 'db4' with level six decomposition demonstrated high performance in the region-based classification.  
For future works, we will expand our study through extensive analyses, exploring individual disabilities compared to healthy conditions across channels and region-based classifications while considering age and gender differences. Additionally, we will conduct multiclass classifications of neurological disabilities across individual channels. The integration of deep learning techniques will also be explored as a means to distinguish between disabilities using the proposed features. Furthermore, we plan to test longer EEG segmentation lengths, such as 5 s, 10 s, 20 s, 30 s, and 60 s, with various wavelets to determine their effectiveness for analyzing neurological disabilities. This will enable us to examine the relationship between wavelets and decomposition levels, uncovering the distinctive characteristics associated with different disabilities.  
Conceptualization, S.Y.J., S.J., A.M.P., K.K. and D.H.J.; methodology, S.Y.J., A.M.P., K.K. and D.H.J.; software, S.Y.J. and D.H.J.; validation, S.Y.J., D.H.J. and S.J.; formal analysis, S.Y.J., S.J., A.M.P., K.K. and D.H.J.; writing--original draft preparation S.Y.J. and D.H.J.; writing--review and editing, S.Y.J., S.J., A.M.P., K.K. and D.H.J.; visualization, D.H.J. and S.Y.J.; funding acquisition, S.Y.J., S.J., A.M.P. and K.K. All authors have read and agreed to the published version of the manuscript.  
This material is based upon work supported by the National Science Foundation under Grant No. (2219634).  
Not applicable.  
Publicly available datasets were analyzed in this study. This data can be found here: [[https://brainclinics.com/resources/](https://brainclinics.com/resources/)]([https://brainclinics.com/resources/](https://brainclinics.com/resources/)) (accessed on 14 November 2023).  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.