Alcoholism Identification by Processing the EEG Signals Using Oscillatory Modes Decomposition and Machine Learning
Subject
Alcoholism identificationElectroencephelogram
oscillatory modes decomposition
Feature ectraction
Mavhine learning
Date
2025-01
Metadata
Show full item recordAbstract
Excessive alcoholism has the potential to disturb the nervous system. A timely identification and prevention can cure it. This work proposes a novel approach to detect alcoholism by processing the electroencephalogram (EEG) signals. It is based on the signal processing and machine learning algorithm. We have used the oscillatory mode decompositions to decompose the EEG signals in modes. First six modes are considered for second order difference plots (SODPs). The central tendency measure, area, and mean are chosen as the three features from each intended SODP. Experiments are carried out by taking into account features collected from three different length time windows in order to establish an appropriate EEG signal segment length for the intended application. For classification, different machine learning algorithms are used.Department
Electrical and Computer EngineeringPublisher
ElsevierBook title
Advances in Neural Engineering Volume 2ae974a485f413a2113503eed53cd6c53
https://doi.org/10.1016/B978-0-323-95439-6.00020-X