Deep Learning EEG Cognitive State Detection

Divyanshu Wanjarkar

Title:
Deep Learning EEG Cognitive State Detection

Authors:
Divyanshu Wanjarkar

Cite This Article :

Divyanshu Wanjarkar "Deep Learning EEG Cognitive State Detection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Advancements and Emerging Trends in Computer Applications - Innovations, Challenges, and Future Prospects, March 2025, pp.1591-1596, URL: https://www.ijtsrd.com/papers/ijtsrd81073.pdf

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Abstract :
This study suggests using electroencephalography (EEG) in conjunction with a convolutional neural network (CNN) model to detect major depressive disorder (MDD). In the suggested approach, a CNN model trained to identify the distinctive EEG signs of sadness processed EEG data images with brain activity patterns linked to MDD. Early detection and intervention are made easier by the CNN model's output, which indicates the existence and severity of MDD. This method has the potential to completely change the diagnosis of depression by offering a quicker, more objective, and more accessible way to detect people who are at risk of developing major depressive disorder (MDD). This would improve patient outcomes and lessen the burden of this looming condition on society.

Keywords :
Major Depressive Disorder (MDD) ; Electroencephalography (EEG) ; Convolutional Neural Networks (CNNs).

Publication Details:

Unique Identification Number : IJTSRD81073

Published In : Special Issue | Advancements and Emerging Trends in Computer Applications - Innovations, Challenges, and Future Prospects, March 2025

Page Number(s) : 1591-1596

Publisher Name : IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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