Convolutional Neural Networks Model for Emotion Recognition Using EEG Signal
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Date
2021-04-29
Journal Title
Journal ISSN
Volume Title
Publisher
North Atlantic University Union
Abstract
A Brain-computer interface (BCI) using an
electroencephalogram (EEG) signal has a great attraction
in emotion recognition studies due to its resistance to
humans’ deceptive actions. This is the most significant
advantage of brain signals over speech or visual signals in
the emotion recognition context. A major challenge in
EEG-based emotion recognition is that a lot of effort is
required for manually feature extractor, EEG recordings
show varying distributions for different people and the
same person at different time instances. The Poor
generalization ability of the network model as well as low
robustness of the recognition system. Improving
algorithms and machine learning technology helps
researchers to recognize emotion easily. In recent years,
deep learning (DL) techniques, specifically convolutional
neural networks (CNNs) have made excellent progress in
many applications. This study aims to reduce the manual
effort on features extraction and improve the EEG signal
single model’s emotion recognition using convolutional
neural network (CNN) architecture with residue block.
The dataset is shuffle, divided into training and testing,
and then fed to the model. DEAP dataset has class 1, class
2, class 3, and class 4 for both valence and arousal with an
accuracy of 90.69%, 91.21%, 89.66%, 93.64%
respectively, with a mean accuracy of 91.3%. 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 a mean accuracy of 94.13% on
the SEED dataset. The experimental results indicated that
CNN Based on residual networks can achieve an excellent
result with high recognition accuracy, which is superior to
most recent approaches.
Description
Keywords
Adam optimizer, BCI, Convolutional neural network (CNN), Deep learning (DL), EEG, Emotion recognition, Single model emotion recognition, Residual block