Electroencephalogram Features to Identify the Mental Disorders

The quantitative analysis of electroencephalogram (EEG) is essential for both clinical and neurophysiological research, offering insights into the complex interactions between millions of neurons in the human brain. This study utilizes MATLAB to analyze EEG signals, revealing variations in frequency bands across psychiatric disorders like autism and bipolar disorder. The obtained results are classified and tabulated, applying Kuppuswamy’s socioeconomic scale to assess the severity of recordings. By studying EEG patterns, this research aims to identify unique features that contribute to optimal classification accuracy and provide valuable information for diagnosing and understanding different mental disorders. Randomly selected subjects represent controlled, epilepsy, and Autism Spectrum Disorder groups.