EEG Based Classification of Emotions with CNN and RNN


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.

by 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,

URL: https://www.ijtsrd.com/papers/ijtsrd30374.pdf

Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30374/eeg-based-classification-of-emotions-with-cnn-and-rnn/s-harshitha

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