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Review of EEG-based Classification of Depression Patients

by Yasmeen Anis, Kaptan Singh, Amit Saxena
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 2
Year of Publication: 2023
Authors: Yasmeen Anis, Kaptan Singh, Amit Saxena
10.5120/ijca2023922677

Yasmeen Anis, Kaptan Singh, Amit Saxena . Review of EEG-based Classification of Depression Patients. International Journal of Computer Applications. 185, 2 ( Apr 2023), 42-46. DOI=10.5120/ijca2023922677

@article{ 10.5120/ijca2023922677,
author = { Yasmeen Anis, Kaptan Singh, Amit Saxena },
title = { Review of EEG-based Classification of Depression Patients },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2023 },
volume = { 185 },
number = { 2 },
month = { Apr },
year = { 2023 },
issn = { 0975-8887 },
pages = { 42-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number2/32681-2023922677/ },
doi = { 10.5120/ijca2023922677 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:25:07.752968+05:30
%A Yasmeen Anis
%A Kaptan Singh
%A Amit Saxena
%T Review of EEG-based Classification of Depression Patients
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 2
%P 42-46
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The electroencephalogram, or EEG, plays a significant part in the operation of electronic healthcare systems, particularly in the field of mental healthcare, which places a premium on continuous monitoring that is as unobtrusive as possible. Signals on an EEG may be interpreted to indicate activity going on in a person's brain as well as distinct emotional states. A sensation of mental or bodily strain is what we refer to as stress. It might be anything—an experience or a thought—that provokes feelings of agitation, anger, or nervousness in you. Mental stress has emerged as a significant problem in modern society and has the potential to lead to functional incapacity in the workplace. The study of electroencephalogram (EEG) signals may benefit from the use of a machine learning (ML) framework. This article provides an overview of the categorization of depression patients based on EEG.

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Index Terms

Computer Science
Information Sciences

Keywords

EEG Emotion Stress Machine Learning E-healthcare

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