Performance of k-NN classifier for emotion detection using EEG signals

This paper describes the performance of k-NN classifier to classify the different emotions. The human brain is a superimposition of the diverse processes. This complex structure of brain is recognized through EEG signals. EEG signals indicate the changes in the state of brain. Electroencephalograph (EEG) measurements are commonly used in different research areas under the field of medical. Data acquisition is done for different emotions with the help of ADInsruments´ power lab instrument. The real life EEG signals are collected with the help of Ground Truth Method. In this paper, proposed method consists of four steps, viz., acquisition of data, Pre-processing, Feature extraction and Classification. Subjects are stimulated for Sad and Happy emotions. Statistical features are then given to a k-NN classifier. The k Nearest Neighbor classifier gives different accuracy of classification for different combinations of training and testing dataset. The system has been tested on number of subjects to observe the performance of k-NN classifier.