Quaternary classification of emotions based on electroencephalogram signals using hybrid deep learning model

Recognizing emotions from electroencephalography (EEG) signals is a trustworthy and reliable method to monitor the mental health of patients and the enthusiasm of individual behavioral feelings and polarity. Recently, the researcher focused on classifying EEG emotions from multimodal DEAP data set i...

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Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing 2023-03, Vol.14 (3), p.2429-2441
Main Authors: Singh, Khushboo, Ahirwal, Mitul Kumar, Pandey, Manish
Format: Article
Language:English
Subjects:
Accuracy
Arousal
Artificial Intelligence
Artificial neural networks
Brain research
Classification
Computational Intelligence
Datasets
Deep learning
Electroencephalography
Emotion recognition
Emotions
Engineering
Machine learning
Neural networks
Original Research
Physiology
Robotics and Automation
Support vector machines
User Interfaces and Human Computer Interaction
Wavelet transforms
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Summary:Recognizing emotions from electroencephalography (EEG) signals is a trustworthy and reliable method to monitor the mental health of patients and the enthusiasm of individual behavioral feelings and polarity. Recently, the researcher focused on classifying EEG emotions from multimodal DEAP data set into binary (high and low) and ternary (low, medium and high) classes based on valence and arousal scale. However, for deep and intrinsic emotion recognition, multiclass classification is preferred. But non-linearity and non-stationary nature of EEG make it more challenging. Therefore, this paper proposes a hybrid deep learning model of one-dimensional Convolutional Neural Network (1DCNN) and Bidirectional Long Short-Term Memory (BI-LSTM) for multiclass emotion classification. The hybrid model linearly distributed desired class labels over two-dimensional emotion space (valence and arousal) as happy, relaxed, anger, and sad. The simulation results of the proposed model for binary, ternary and quaternary classification of emotion acquire 91.31%, 89.32% and 88.19% accuracy, respectively. Subject wise analysis of results has also been performed. The novelty of this work is the quaternary classification of emotions.
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-022-04495-4