Python (deep learning and machine learning) for EEG signal processing on the example of recognizing the disease of alcoholism

TL;DR

This paper introduces a neural network-based method for quick, anonymous alcoholism detection using EEG signals, leveraging deep learning and signal processing techniques like wavelet and Fourier transforms.

Contribution

It presents a novel EEG-based neural network approach for anonymous alcoholism diagnosis that does not require private information or physical contact.

Findings

Deep neural networks can classify alcoholic and control groups with high accuracy.

EEG correlation signals are effective features for alcoholism detection.

The method ensures privacy and reduces potential deception in diagnosis.

Abstract

Alcoholism is one of the most common diseases in the world. This type of substance abuse leads to mental and physical dependence on ethanol-containing drinks. Alcoholism is accompanied by progressive degradation of the personality and damage to the internal organs. Today still not exists a quick diagnosis method to detect this disease. This article presents the method for the quick and anonymous alcoholism diagnosis by neural networks. For this method, don't need any private information about the subject. For the implementation, we considered various algorithms of machine learning and deep neural networks. In detail analyzed the correlation of the signals from electrodes by neural networks. The wavelet transforms and the fast Fourier transform was considered. The manuscript demonstrates that the deep neural network which operates only with a dataset of EEG correlation signals can anonymously classify the alcoholic and control groups with high accuracy. On the one hand, this method will allow subjects to be tested for alcoholism without any personal data, which will not cause inconvenience or shame in the subject, and on the other hand, the subject will not be able to deceive specialists who diagnose the subject for the presence of the disease.