Impulsive EEG in Drug Users with Naive Bayesian Classification Approach

Measuring impulsivity is essential for understanding mental health and interpersonal relationships. Identifying impulsivity levels can help in detecting potential mental disorders, such as impulse control disorders or ADHD, which may require further evaluation and treatment. This study aims to develop a classification system using EEG (Electroencephalography) data to assess impulsivity in drug users. The system uses the Naïve Bayes algorithm to classify data into five categories: very active, active, less active, likely active, and inactive. Data were collected from 21 participants and analyzed based on alpha and beta brain waves. The results indicate a classification accuracy of 95.3%, demonstrating the effectiveness of this approach in interpreting brain activity related to drug use and classifying EEG patterns. This research provides practical benefits for understanding impulsivity patterns in drug users, thereby supporting more targeted assessment and treatment processes.