EEG Based Emotion Investigation from Various Brain Region Using Deep Learning Algorithm

Abstract

AbstractEmotion detection using Electroencephalogram (EEG) is a reliable approach for the formulation of human brain signals as this does not depend on humans' posturing or pretentiousness. Analyzing every brain region generates noise, redundant information. This increase more complexities in calculations. Ultimately, handling unwanted and excessive data degrades the detection accuracy as it calls for use of additional and complex hardware. Therefore to overcome this challenge, this paper proposes a detailed study and understanding of different brain regions which contribute most to emotion generation and which are very much vital for emotion analysis. This work provides guidelines in neuroscience and establishes that a particular brain region is vital which provides sufficient information and EEG data for emotion recognition. In this paper features of differential entropy, Hjorth parameters, and power spectral density (PSD) are used to train bidirectional long short term memory (BiLSTM) network. The performance of the proposed technique is evaluated on the DEAP benchmark database for different brain regions namely the prefrontal region, frontal region, temporal region, parietal region, and occipital region. The results show that the frontal brain region has the highest emotion recognition rate compared to other regions and is vital for emotion recognition.KeywordsEmotionsDifferential entropyHjorth parametersPower spectral densityBi-LSTMBrain region