DocumentCode :
3667866
Title :
Performance measure of the multi-class classification for the EEG calmness categorization study
Author :
Siti Armiza Mohd Aris;Aisyah Hartini Jahidin;Mohd Nasir Taib
Author_Institution :
UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
fYear :
2015
fDate :
5/1/2015 12:00:00 AM
Firstpage :
134
Lastpage :
139
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.
Keywords :
"Electroencephalography","Accuracy","Sensitivity","Indexes","Testing","Training","Conferences"
Publisher :
ieee
Conference_Titel :
BioSignal Analysis, Processing and Systems (ICBAPS), 2015 International Conference on
Type :
conf
DOI :
10.1109/ICBAPS.2015.7292233
Filename :
7292233
Link To Document :
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