Analysis of Feature Extraction Models for Emotion Recognition using EEG Signals

Abstract

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Electroencephalogram (EEG) based affective brain-computer interface (a-BCI) systems are gaining interest among researchers in recent decades. a-BCI systems interpret/recognize human emotions using features extracted from the EEG signals. Hence, features play a crucial role in building EEG based emotion recognition models. As a result, analysis of feature extraction models becomes inevitable. In this work, we have proposed an analysis model for analyzing the feature extraction models for EEG based emotion recognition with the help of the DEAP dataset. Features were extracted using manual feature extraction techniques and the convolutional neural network. Several combinations of feature sets were given as input to classifiers and the results obtained were analyzed with various evaluation metrics. The proposed analysis model will help the researchers to choose the feature extraction model for emotion recognition.