DocumentCode :
3002866
Title :
Investigating principal component analysis for classification of EEG data
Author :
Deepa, V Baby ; Thangaraj, P. ; Chitra, S.
Author_Institution :
M Kumarasamy Coll. of Eng., Karur, India
fYear :
2010
fDate :
11-12 June 2010
Firstpage :
461
Lastpage :
464
Abstract :
The communication system that does not depend on the brain´s normal output pathways of peripheral nerves and muscles is known as Brain Computer Interaction (BCI). Therefore, BCI system can provide an augmentative communication method for patients with severe motor disabilities. An Electroencephalogram (EEG) is a recording of the very weak (on the order of 5-100 μV) electrical potentials generated by the brain on the scalp. An EEG is recorded as a potential difference between a signal electrode placed on the scalp and a reference electrode (generally connected to one ear or both ears).
Keywords :
brain-computer interfaces; electroencephalography; pattern classification; principal component analysis; EEG data classification; brain computer interaction; brain normal output pathways; electroencephalogram; motor disabilities; peripheral nerves; principal component analysis; signal electrode; Computer peripherals; Ear; Educational institutions; Electrodes; Electroencephalography; Information technology; Muscles; Principal component analysis; Scalp; Vectors; BCI; EEG; PCA; SMO;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Networking and Information Technology (ICNIT), 2010 International Conference on
Conference_Location :
Manila
Print_ISBN :
978-1-4244-7579-7
Electronic_ISBN :
978-1-4244-7578-0
Type :
conf
DOI :
10.1109/ICNIT.2010.5508471
Filename :
5508471
Link To Document :
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