###### Abstract  
Neurological disabilities cause diverse health and mental challenges, impacting quality of life and imposing financial burdens on both the individuals diagnosed with these conditions and their caregivers. Abnormal brain activity, stemming from malfunctions in the human nervous system, characterizes neurological disorders. Therefore, the early identification of these abnormalities is crucial for devising suitable treatments and interventions aimed at promoting and sustaining quality of life. Electroencephalogram (EEG), a non-invasive method for monitoring brain activity, is frequently employed to detect abnormal brain activity in neurological and mental disorders. This study introduces an approach that extends the understanding and identification of neurological disabilities by integrating feature extraction, machine learning, and visual analysis based on EEG signals collected from individuals with neurological and mental disorders. The classification performance of four feature approaches--EEG frequency band, raw data, power spectral density, and wavelet transform--is assessed using machine learning techniques to evaluate their capability to differentiate neurological disabilities in short EEG segmentations (one second and two seconds). In detail, the classification analysis is conducted under two conditions: single-channel-based classification and region-based classification. While a clear demarcation between normal (healthy) and abnormal (neurological disabilities) EEG metrics may not be evident, their similarities and distinctions are observed through visualization, employing wavelet features. Notably, the frontal brain region (frontal lobe) emerges as a crucial area for distinguishing abnormalities among different brain regions. Also, the integration of wavelet features and visual analysis proves effective in identifying and understanding neurological disabilities. neurological disorders; discrete wavelet transform; visual analysis; machine learning; feature extraction +
Footnote †: journal: Medical  
significantly impacts patients' lives, leading to depression, presenteeism, and financial burdens on families or caregivers [5]. Major depressive disorder (MDD), the most prevalent mental disorder, adversely affects physical health, sleep, mood, and social activities, potentially leading to suicidal thoughts or attempts [6]. Obsessive-compulsive disorder (OCD), another common mental disorder, diminishes quality of life through persistent intrusive thoughts and repetitive behaviors [7,8]. The direct economic cost of mental or neurological disorders in the United States surpassed USD 210 billion in 2010, with the global economic cost of mental disorders estimated at USD 2.5 trillion [9,10]. Since neurological and mental disorders are increasingly prevalent and closely related to various mental and physical health conditions and deficits in social interaction, the early detection of abnormal conditions is crucial for effective care planning, intervention, and advocacy. EEG is capable of early diagnosis identification and is reliable and valid in accurately detecting neurological and mental disorders. While previous studies have adeptly utilized EEG data for early detection, these studies primarily focused on analyzing and computing techniques for isolating single disorders. Moreover, despite the existence of more than six hundred neurological diseases, limited research has been conducted to identify neurological disorders using a unified framework. This study shifts the focus to comprehending the patterns associated with multiple neurological disabilities by extracting significant features and subjecting them to analysis to enhance our understanding of these disabilities through visualization techniques. We introduce an automated pattern recognition approach that integrates visual analysis and machine learning to analyze neurological disabilities. It is important to note that, in this study, the term 'neurological disability' encompasses both neurological disorders and mental disorders.