Brain Computer Interface for Emotion Recognition Based on EEG Signal

Abstract

This paper presents an emotion recognition system based on electroencephalography (EEG) signals. This system helps medical practitioners to analyse the mental health of an individual. Eight healthy volunteers/ subjects had participated in this experiment. A specific feeling is evoked using particular songs and videos that are collected to present before the subjects. Total 6 emotions namely neutral, happy, sad, disgust, fear and motivate are captured and analysed. Data is classified using eighteen statistical features. The sampling rate is 1200Hz. Signals are filtered using pre-processing techniques. Frequency, time and timefrequency domain features are extracted. An array of 10 classifiers is used including Decision Tree, Random Forest, Optimised Random Forest, Logistic regression, Support Vector Machine (SVM) Polynomial, SVM Sigmoid, SVM RBF, K-Nearest Neighbours, Gaussian NB, Gradient Boosting Classifier. Accuracy, recall, precision, and F1 score are employed as performance metrics. The accuracy obtained for SVM classifier was 79.34%.