Performance measure of the multi-class classification for the EEG calmness categorization study

Abstract

This study presents a small part of the major study, involved in categorizing EEG calmness. The kNN classifier was used to classify EEG features named as asymmetry index (AsI) which was extracted during relaxed state and non-relaxed state. Results from the previous study showed that the EEG behaviour during both states appear to have more than two groups. The group of four EEG behaviours and three EEG behaviours which was clustered by FCM was validated through kNN. However, to investigate the kNN classification accuracy, the classifier performance measure is essential. Thus for this study purposes, performance measure of the kNN was tested using confusion matrix. Result of performance measure indicates that kNN provide 100% accuracy on three clusters of behaviours which could be proposed as calmness index.