Deep Learning Based on CNN for Emotion Recognition Using EEG Signal
No Thumbnail Available
Date
2021-04-14
Journal Title
Journal ISSN
Volume Title
Publisher
WSEAS
Abstract
Emotion recognition based on brain-computer interface (BCI) has attracted important
research attention despite its difficulty. It plays a vital role in human cognition and helps in making
the decision. Many researchers use electroencephalograms (EEG) signals to study emotion because of
its easy and convenient. Deep learning has been employed for the emotion recognition system. It
recognizes emotion into single or multi-models, with visual or music stimuli shown on a screen. In
this article, the convolutional neural network (CNN) model is introduced to simultaneously learn the
feature and recognize the emotion of positive, neutral, and negative states of pure EEG signals single
model based on the SJTU emotion EEG dataset (SEED) with ResNet50 and Adam optimizer. The
dataset is shuffle, divided into training and testing, and then fed to the CNN model. The negative
emotion has the highest accuracy of 94.86% fellow by neutral emotion with 94.29% and positive
emotion with 93.25% respectively. With average accuracy of 94.13%. The results showed excellent
classification ability of the model and can improve emotion recognition.
Description
Keywords
BCI, EEG, Deep learning (DL), CNN, ResNet50, Emotion recognition, Adam optimizer, SEED