Electroencephalography-based classification of human emotion: a hybrid strategy in machine learning paradigm

摘要

The objective if this article is to develop a new improved two stage method for classifying emotional states of human by fusing back-propagation artificial neural network (BPANN) and k-nearest neighbours (k-NN). A publicly available electroencephalogram (EEG) signal database for emotion analysis using physiological signals is used in experiments. The EEG signals are initially pre-processed followed by feature extraction in time domain and frequency domain. The extracted features were then supplied to proposed model for emotion recognition. The proposed machine learning framework attains higher classification accuracy of 78.33 % as compared to conventional BPANN and k-NN classifiers, which achieves classification accuracy of 56.90 % and 59.52 % respectively. Future work is required to evaluate the proposed model in practical scenario wherein a proficient psychologist or medical professional can analyse the emotion recognised by first stage and the unsure test cases can be supplied to secondary classifier (k-NN) for further assessment.