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

fYear

2011

fDate

March 30 2011-April 1 2011

Firstpage

54

Lastpage

58

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/UKSIM.2011.20

Filename

5754207