Maryam Omri* and Wided Lejouad Chaari

LARIA Laboratory, National School of Computer Sciences (ENSI), University of Manouba, Tunisia

*Corresponding Author: Maryam Omri, LARIA Laboratory, National School of Computer Sciences (ENSI), University of Manouba, Tunisia.

Received: September 28, 2022; Published: May 26, 2023

Abstract

Brain Computer Interfaces (BCI) are computer systems that capture and analyze brain signals. Out of the several techniques used for capturing brain signals, electroencephalogram (EEG) has been regarded as one of the most promising techniques. A lot of models have been investigated for extracting different emotions based on EEG signals. However, these models suffer from ignorance of spatial dimensions which indicate the location of each channel such as the asymmetry between the electrode pairs and other salient information related to emotional states. In this paper, a deep learning approach based on Fast Fourier Transform (FFT), Power Spectral Density (PSD) and a Capsule Network (CapsNet) is proposed. In order to represent the EEG signals better, we firstly apply FFT to extract the EEG frequency bands Delta, Thêta, Alpha, Bêta and Gamma. Then, Power Spectral Density (PSD) is introduced for describing the activation level of an EEG signal and improving the efficiency of emotion classification. Finally, features extracted are fed into CapsNet for the classification of emotional states. Experiments on the DEAP dataset show that the proposed method achieved 95% accuracy which improves the efficiency of capsule neural networks.

 Keywords: EEG Signal; Power Spectral Density; Deep Learning; CapsNet; Emotion Recognition

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Citation

Citation: Maryam Omri and Wided Lejouad Chaari. “Emotion Recognition from EEG Signals Based on Caps Net Neural Network”.Acta Scientific Medical Sciences 7.6 (2023): 106-113.

Copyright

Copyright: © 2023 Maryam Omri and Wided Lejouad Chaari. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.