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. 2024 May 6;14(1):10371.
doi: 10.1038/s41598-024-60977-9.

Detecting emotions through EEG signals based on modified convolutional fuzzy neural network

Nasim Ahmadzadeh Nobari Azar  1   2 , Nadire Cavus  3   4 , Parvaneh Esmaili  5 , Boran Sekeroglu  6 , Süleyman Aşır  7   8
Affiliations

Detecting emotions through EEG signals based on modified convolutional fuzzy neural network

Nasim Ahmadzadeh Nobari Azar et al. Sci Rep. .

Abstract

Emotion is a human sense that can influence an individual's life quality in both positive and negative ways. The ability to distinguish different types of emotion can lead researchers to estimate the current situation of patients or the probability of future disease. Recognizing emotions from images have problems concealing their feeling by modifying their facial expressions. This led researchers to consider Electroencephalography (EEG) signals for more accurate emotion detection. However, the complexity of EEG recordings and data analysis using conventional machine learning algorithms caused inconsistent emotion recognition. Therefore, utilizing hybrid deep learning models and other techniques has become common due to their ability to analyze complicated data and achieve higher performance by integrating diverse features of the models. However, researchers prioritize models with fewer parameters to achieve the highest average accuracy. This study improves the Convolutional Fuzzy Neural Network (CFNN) for emotion recognition using EEG signals to achieve a reliable detection system. Initially, the pre-processing and feature extraction phases are implemented to obtain noiseless and informative data. Then, the CFNN with modified architecture is trained to classify emotions. Several parametric and comparative experiments are performed. The proposed model achieved reliable performance for emotion recognition with an average accuracy of 98.21% and 98.08% for valence (pleasantness) and arousal (intensity), respectively, and outperformed state-of-the-art methods.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Summary of the DEAP dataset content.
Figure 2
Figure 2
The proposed framework of emotion recognition in the current study.
Figure 3
Figure 3
Structure of the proposed convolutional fuzzy neural network.
Figure 4
Figure 4
Graphical visualization of comparison results.
See this image and copyright information in PMC

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