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.