Research Area:  Machine Learning
Emotions composed of cognizant logical reactions toward various situations. Such mental responses stem from physiological, cognitive, and behavioral changes. Electroencephalogram (EEG) signals provide a noninvasive and nonradioactive solution for emotion identification. Accurate and automatic classification of emotions can boost the development of human-computer interface. This article proposes automatic extraction and classification of features through the use of different convolutional neural networks (CNNs). At first, the proposed method converts the filtered EEG signals into an image using a time-frequency representation. Smoothed pseudo-Wigner-Ville distribution is used to transform time-domain EEG signals into images. These images are fed to pretrained AlexNet, ResNet50, and VGG16 along with configurable CNN. The performance of four CNNs is evaluated by measuring the accuracy, precision, Mathews correlation coefficient, F1-score, and false-positive rate. The results obtained by evaluating four CNNs show that configurable CNN requires very less learning parameters with better accuracy. Accuracy scores of 90.98%, 91.91%, 92.71%, and 93.01% obtained by AlexNet, ResNet50, VGG16, and configurable CNN show that the proposed method is best among other existing methods.
Author(s) Name:  Smith K. Khare; Varun Bajaj
Journal name:  IEEE Transactions on Neural Networks and Learning Systems
Publisher name:  IEEE
Volume Information:  ( Volume: 32, Issue: 7, July 2021) Page(s): 2901 - 2909
Paper Link:   https://ieeexplore.ieee.org/abstract/document/9153955