Title :
Classification of psychophysiological conditions using k-Nearest Neighbour
Author :
Aris, Siti Armiza Mohd ; Taib, M.N.
Author_Institution :
UTM Razak Sch. of Eng. & Adv. Technol., Univ. Teknol. Malaysia, Kuala Lumpur, Malaysia
Abstract :
In this study, the approximate behaviour during resting state and mental task state are predicted to be grouped via asymmetry score. This is realized by applying an advance signal processing technique via alpha spectral. Subtractive clustering is utilized in order to confirm the maximum number of cluster center existed in the combined features of resting state and mental task state. Result showed five cluster centers presented within the data. Hence, the FCM algorithm is sets to generate five clusters, four clusters and three clusters. kNN is chosen to grade the data set of cluster and result showed that three clusters give 100% classification.
Keywords :
electroencephalography; fuzzy set theory; learning (artificial intelligence); medical signal processing; neurophysiology; pattern clustering; signal classification; EEG signal; FCM algorithm; advance signal processing technique; alpha spectral; asymmetry score; electroencephalographic signal; fuzzy c-means algorithm; k-nearest neighbour; mental task state; psychophysiological conditions classification; resting state; subtractive clustering; Clustering algorithms; Conferences; Electrodes; Electroencephalography; Euclidean distance; Indexes; Systems engineering and theory; Asymmetry Score; EEG; FCM; Subtractive Clustering; kNN;
Conference_Titel :
System Engineering and Technology (ICSET), 2013 IEEE 3rd International Conference on
Conference_Location :
Shah Alam
Print_ISBN :
978-1-4799-1028-1
DOI :
10.1109/ICSEngT.2013.6650152
