Title :
Feature Extraction of EEG Signals and Classification Using FCM
Author :
Aris, Siti Armiza Mohd ; Taib, Mohd Nasir ; Lias, Sahrim ; Sulaiman, Norizam
Author_Institution :
Razak Sch. of Eng., Univ. Teknol. Malaysia, Kuala Lumpur, Malaysia
fDate :
March 30 2011-April 1 2011
Abstract :
EEG data were collected between two conditions, relax wakefulness (close-eyes) and non-relax (IQ test). Data segmentation and linear regression model is used to extract the EEG features and to obtain the slope and the mean relative power from 43 participants. All of the data were then normalized and classified using Fuzzy C-Means (FCM) clustering. Results shown that there are different of activities exist in the EEG which proved that the feature extraction using linear regression model manage to discern between two different brain behaviors.
Keywords :
electroencephalography; feature extraction; regression analysis; EEG signals; FCM; data segmentation; feature extraction; fuzzy C-means clustering; linear regression model; Brain modeling; Electroencephalography; Emotion recognition; Feature extraction; Humans; Linear regression; EEG; FCM; Linear Regression Technique;
Conference_Titel :
Computer Modelling and Simulation (UKSim), 2011 UkSim 13th International Conference on
Conference_Location :
Cambridge
Print_ISBN :
978-1-61284-705-4
Electronic_ISBN :
978-0-7695-4376-5
DOI :
10.1109/UKSIM.2011.20
