Hybrid Feature Extraction Model for Emotion Recognition Using EEG Signals and Deep Learning Approaches

Emotions reflect the mental state of a person. Individual changes in physiological, physical, mental and behavioral factors reflect different types of emotions. Studies on emotion recognition always have attention among researchers. Signal processing and feature handling techniques are developed for accurate emotions recognition from the biological brain signals. Through electroencephalography (EEG) channels, physiological signals are obtained and the essential features are extracted for analysis. However, the detection or recognition performance of traditional methods provides room for improvement due to poor accuracy or improper feature handling performances. The EEG signals for emotion recognition are predicted from two data sources that are preprocessed through filtering method for reducing artifacts of the EEG signals. Then, the preprocessed signals are given to the feature extraction phase such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM). The CNN model is applied for the extraction of statistical related features and the LSTM model is applied to extract non-linear related features. These features are fused at the final stage of the feature extraction phase and ResNet152 model is implemented in this paper for classifying types of emotions in the EEG signals according to the extracted features. The comprehensive analyses are performed through different performance evaluation measures and the proposed model attained better performances of 0.9867 and 0.9646 from accuracy and mathew’s correlation coefficient respectively. From this experimental validation, the proposed model achieved better outcome than other compared existing approaches.