Deep learning method for selecting effective models and feature groups in emotion recognition using an asian multimodal database

Abstract

Emotional awareness is vital for advanced interactions between humans and computer systems. This paper introduces a new multimodal dataset called MERTI-Apps based on Asian physiological signals and proposes a genetic algorithm (GA)—long short-term memory (LSTM) deep learning model to derive the active feature groups for emotion recognition. This study developed an annotation labeling program for observers to tag the emotions of subjects by their arousal and valence during dataset creation. In the learning phase, a GA was used to select effective LSTM model parameters and determine the active feature group from 37 features and 25 brain lateralization features extracted from the electroencephalogram (EEG) time, frequency, and time–frequency domains. The proposed model achieved a root-mean-square error (RMSE) of 0.0156 in terms of the valence regression performance in the MAHNOB-HCI dataset, and RMSE performances of 0.0579 and 0.0287 in terms of valence and arousal regression performance, and 65.7% and 88.3% in terms of valence and arousal accuracy in the in-house MERTI-Apps dataset, which uses Asian-population-specific 12-channel EEG data and adds an additional brain lateralization (BL) feature. The results revealed 91.3% and 94.8% accuracy in the valence and arousal domain in the DEAP dataset owing to the effective model selection of a GA.