Emotion Recognition Using Continuous Wavelet Transform and Ensemble of Convolutional Neural Networks through Transfer Learning from Electroencephalogram Signal
Purpose: Emotions are integral brain states that can influence our behavior, decision-making, and functions. Electroencephalogram (EEG) is an appropriate modality for emotion recognition since it has high temporal resolution and is a noninvasive and cheap technique.
Materials and Methods:.A novel approach based on Ensemble pre-trained Convolutional Neural Networks (ECNNs) is proposed to recognize four emotional classes from EEG channels of individuals watching music video clips. First, scalograms are built from one-dimensional EEG signals by applying the Continuous Wavelet Transform (CWT) method. Then, these images are used to re-train five CNNs: AlexNet, VGG-19, Inception-v1, ResNet-18, and Inception-v3. Then, the majority voting method is applied to make the final decision about emotional classes. The 10-fold cross-validation method is used to evaluate the performance of the proposed method on EEG signals of 32 subjects from the DEAP database.
Results:.The experiments showed that applying the proposed ensemble approach in combinations of scalograms of frontal and parietal regions improved results. The best accuracy, sensitivity, precision, and F-score to recognize four emotional states achieved 96.90%±0.52, 97.30±0.55, 96.97±0.62, and 96.74±0.56, respectively.
Conclusion: So, the newly proposed model from EEG signals improves recognition of the four emotional states in the DEAP database.
|Issue||Vol 10 No 1 (2023)|
|Emotion Recognition Electroencephalogram Deep Learning Transfer Learning Ensemble Approach Continuous Wavelet Transform|
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|This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.|