EEG Based Classification of Emotions with CNN and RNN

Mrs. A. Selvarani; S. Harshitha

Title:
EEG Based Classification of Emotions with CNN and RNN

Authors:
S. Harshitha | Mrs. A. Selvarani

Cite This Article :

S. Harshitha | Mrs. A. Selvarani "EEG Based Classification of Emotions with CNN and RNN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4, June 2020, pp.1289-1293, URL: https://www.ijtsrd.com/papers/ijtsrd30374.pdf

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Abstract :
Emotions are biological states associated with the nervous system, especially the brain brought on by neurophysiological changes. They variously cognate with thoughts, feelings, behavioural responses, and a degree of pleasure or displeasure and it exists everywhere in daily life. It is a significant research topic in the development of artificial intelligence to evaluate human behaviour that are primarily based on emotions. In this paper, Deep Learning Classifiers will be applied to SJTU Emotion EEG Dataset (SEED) to classify human emotions from EEG using Python. Then the accuracy of respective classifiers that is, the performance of emotion classification using Convolutional Neural Network (CNN) and Recurrent Neural Networks are compared. The experimental results show that RNN is better than CNN in solving sequence prediction problems.

Keywords :
Emotion classification, SEED, EEG, CNN, RNN, Confusion matrix

Publication Details:

Unique Identification Number : IJTSRD30374

Published In : Volume-4 | Issue-4, June 2020

Page Number(s) : 1289-1293

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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