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EEG-Based of Drug Addiction Levels Using Adaptive Neuro-Fuzzy Inference
Abstract
Drug addiction influences the brain with varying levels of intensity. After being consumed, the active compounds in drugs are transported through the bloodstream to the brain, where they interfere with neural mechanisms and weaken impulse regulation. These substances primarily affect the limbic system, producing emotional reactions and sensations of pleasure that alter an individual’s mental condition. Electroencephalogram (EEG) analysis provides a reliable means of studying addiction, as it reflects brainwave patterns associated with addictive behavior. The classification of drug addiction levels in this research was conducted using ANFIS, with input features obtained from the five brainwave ranges of delta, theta, alpha, beta-1, and beta-2. The approach achieved an accuracy rate of 94.2%, highlighting its effectiveness in detecting neural markers of addiction and its potential contribution to the development of more precise diagnostic tools and intervention methods.
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