Abstract
The main aim of the article is to discuss the discriminative features of EEG for alcoholism analysis. EEG signals are non-stationary and complex for analysis. The paper reports a brief discussion on different feature extraction and selection methods that have been studied for alcoholism analysis. Features can be extracted from direct EEG signals or decomposing signals into EEG bands. A wide range of features can be obtained from EEG. The paper reports a review on various feature selection techniques like statistical t-test, Mann-Whitney U test, Kruskal-Wallis plot, and principal component analysis. Reported results in the literature indicate the necessity of proper feature extraction and feature selection.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
