An integrated entropy-spatial framework for automatic gender recognition enhancement of emotion-based EEGs

Investigating gender differences based on emotional changes using electroencephalogram (EEG) is essential to understand various human behavior in the individual situation in our daily life. However, gender differences based on EEG and emotional states are not thoroughly investigated. The main novelty of this paper is twofold. First, it aims to propose an automated gender recognition system through the investigation of five entropies which were integrated as a set of entropy domain descriptors (EDDs) to illustrate the changes in the complexity of EEGs. Second, the combination EDD set was used to develop a customized EEG framework by estimating the entropy-spatial descriptors (ESDs) set for identifying gender from emotional-based EEGs. The proposed methods were validated on EEGs of 30 participants who examined short emotional video clips with four audio-visual stimuli (anger, happiness, sadness, and neutral). The individual performance of computed entropies was statistically examined using analysis of variance (ANOVA) to identify a gender role in the brain emotions. Finally, the proposed ESD framework performance was evaluated using three classifiers: support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF), and long short-term memory (LSTM) deep learning model. The results illustrated the effect of individual EDD features as remarkable indices for investigating gender while studying the relationship between EEG brain activity and emotional state changes. Moreover, the proposed ESD achieved significant enhancement in classification accuracy with SVM indicating that ESD may offer a helpful path for reliable improvement of the gender detection from emotional-based EEGs.