Classification of Human Emotions using Multiwavelet Transform based Features and Random Forest Technique

Abstract

Objective: To extract statistical features from EEG signal for human emotion recognition. Methods: Emotions, the “inner” state of a person play a vital role in analysing the state of mind. In this paper, a new method for human emotion recognition using Multi Wavelet Transform (MWT) and random forest (ensemble technique) for classification of human emotions from EEG signals has been proposed. The experiment is conducted on EEG database for emotions i.e., DEAP data set. In present work, the data set used contains EEG recording from 4 participants (S01, S02, S03, S04) from 15 channel (FP1, FP2, F3, P3, F4, T7, T8, P4, O1, PZ, PO3, O2, P7, CP2, and C4) having 40 trails each. The paper explore the capability of proposed features namely Mean, Standard deviation, Variance, Shannon entropy, Hjorth parameters and Band power. Findings: The feature set obtained using Multi-Wavelet are then used as input for MLP, KNN, MC-SVM with Puk kernel function and Random Forest (ensemble) classifier for the classification of emotions. The classification accuracy for different emotion state happy 99.8%, sad 98.3%, exciting 95%, hate 96.4% are obtained by the proposed method. Overall 98.1% for classification of emotions (happy, sad, exciting, hate) from EEG signals. Application: The findings are helpful in monitoring alertness, cognitive engagement of patients with impaired motor functions; recognition of emotions; investigating sleep disorders and physiology. In future, efficient feature extraction algorithm using different multiwavelet functions and with a different set of features which include the temporal dynamics of brain signals in the human cognitive system can be considered.