CNN based efficient approach for emotion recognition

Determining the psychophysiological state of people has been a significant issue in many fields, such as the adaptation of disabled people to social life. Recently, various physiological signals have been used in emotion recognition studies since the desired success is limited in studies conducted with traditional methods such as posture and facial expressions. In this study, a GoogLeNet-based deep learning method that can automatically detect human emotions using Electroencephalogram (EEG) signals is proposed. EEG signals were converted to EEG images (scalogram) with Continuous Wavelet Transform (CWT), which is more sensitive to Time Frequency (TF) changes in EEG signals. Then, feature extraction was performed from EEG images with a pre-trained GoogLeNet. Finally, the deep features obtained were applied to popular machine learning methods such as k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Extreme Learning Machine (ELM) classifiers for emotion classification. The proposed method was tested using the GAMEEMO dataset. This study regards two emotions, namely ‘Positive’ and ‘Negative’ for classification. The comprehensive experimental results show that the proposed method achieved 98.78%, 98.53%, and 98.41% emotion detection accuracy in SVM, k-NN, and ELM classifiers, respectively. The proposed method shows improved performance of about 21.85% than of the existing state-of-the-art methods using the same dataset.