Emotion recognition from EEG signals using empirical mode decomposition and second-order difference plot

Abstract

Emotion recognition from electroencephalography (EEG) signals is a very cost-effective method to monitor the general well-being of an individual, an employee of an organization, or to cater to mental health patients. But it is a challenging task owing to the non-stationarity of the EEG signals. Extracting relevant features through signal processing techniques that can be used to classify patterns in the EEG signal leading to different emotions is a difficult task. A dataset for emotion analysis with physiological signals DEAP consists of EEG signals of 32 participants are categorized on the quadrant of valence, arousal, dominance, and liking, which signifies how they are associated with different emotions. In this paper, an efficient classifier for emotion/quadrant recognition from EEG signals with exceptional accuracy is presented. The data preprocessing strategy adapted is Empirical Mode Decomposition (EMD), which decomposes the signals into several oscillatory Intrinsic Mode Functions (IMF). Features are extracted from Second order difference plots (SODP) are area, mean, and central tendency measure of the elliptical region. Wilcoxson Test was performed to ensure the statistical significance of the extracted features with p < 0.05. Support Vector Machine (SVM) and 2-hidden layer Multilayer Perceptron is used for binary and multi-class classification of emotions in the quadrant of valence, arousal, dominance, and liking. The performance of the models is evaluated by statistical parameters- sensitivity, specificity, and accuracy. The classification results from Multilayer Perceptron outperformed that of SVM, and the maximum accuracy achieved, is 100 % in the binary classification of High and Low Arousal space.