Paper Title
Classification Of Emotions From Eeg Using K-Nn Classifier

Abstract
This paper describes a method for automatic classification of different human emotions obtained using Electroencephalograph (EEG) signals. The human brain is a complex system. The superimposition of the diverse processes in the brain is recognized through EEG signals. Electroencephalographic measurements are commonly used in medical applications and in the research areas to study and analyse different disorders in the brain functioning. EEG signals indicate changes in the state of brain. Data acquisition is done for different emotions with the help of ADinstruments’ power lab instrument. In this research work, we have collected real life EEG signals using Ground Truth Method. Our proposed system consists of four steps, viz., Data Acquisition, Pre-processing, Feature extraction and Classification. The subjects were stimulated for different emotions such as Sad and Happy. The signals are pre-processed and used to calculate statistical features which will be given to the classifier. The system has been tested on number of subjects who were stimulated for invoking various emotions. Keywords— Brain Computer Interfacing, EEG signals Electroencephalography, Emotions, k-NN classifier, Statistical Features