Emotion Recognition Using a Novel Granger Causality Quantifier and Combined Electrodes of EEG.

Abstract

Electroencephalogram (EEG) connectivity patterns can reflect neural correlates of emotion. However, the necessity of evaluating bulky data for multi-channel measurements increases the computational cost of the EEG network. To date, several approaches have been presented to pick the optimal cerebral channels, mainly depending on available data. Consequently, the risk of low data stability and reliability has increased by reducing the number of channels. Alternatively, this study suggests an electrode combination approach in which the brain is divided into six areas. After extracting EEG frequency bands, an innovative Granger causality-based measure was introduced to quantify brain connectivity patterns. The feature was subsequently subjected to a classification module to recognize valence-arousal dimensional emotions. A Database for Emotion Analysis Using Physiological Signals (DEAP) was used as a benchmark database to evaluate the scheme. The experimental results revealed a maximum accuracy of 89.55%. Additionally, EEG-based connectivity in the beta-frequency band was able to effectively classify dimensional emotions. In sum, combined EEG electrodes can efficiently replicate 32-channel EEG information.