Hybrid method for optimizing emotion recognition models on electroencephalogram signals

Abstract

Two critical factors that need to be studied in emotion recognition are the differences in electroencephalogram (EEG) signal patterns caused by participant characteristics and EEG signals with spatial information. These factors significantly affect the resulting accuracy. The model proposed in this study can consider these factors. This model consists of the modified weighted mean filter method for the basic EEG signal smoothing process, the differential entropy method for the feature extraction process, the relative difference method for the baseline reduction, the 3D cube method for feature representation, and the continuous capsule network method for the classification process. Based on testing on three public datasets, this hybrid method can overcome factors affecting emotion recognition accuracy. This statement is based on the accuracy produced by this model, which outperformed the accuracy validated in previous studies.