Classification of Human Emotions from EEG Signals using Filtering and ANFIS Classifier

Human emotion classification has been an attractive research area in the field of data mining. Several research works have been carried out for investigating the classification system for human emotions. This research article is intended to deal with the classification of human emotions through Electroencephalogram (EEG). The essential aspects of this system are feature extraction and accurate classification of the emotion-related EEG-characteristics to attain a significant emotion recognition system. In this research work, the emotions and the patterns of EEG signals of human brain are studied. The aim of this work is to investigate the changes in the brain signals in the domain of different emotions. The study can be analyzed for its utility in the diagnosis of psychosomatic disorders like anxiety and depression in economical way with higher precision. The modified adaptive filtering for signal preprocessing is proposed in this system for removing the noise and artifacts in EEG signal. The adaptive neuro fuzzy inference system is also proposed for classifying and analyzing the emotions based on the features selected. The proposed system is compared with the existing system and the performance of the proposed system is evaluated using the metrics such as specificity and sensitivity.