Title :
Two Channel EEG Thought Pattern Classifier
Author :
Craig, D.A. ; Nguyen, H.T. ; Burchey, H.A.
Author_Institution :
Key Univ. Res. Centre, Univ. of Technol., Sydney, NSW
fDate :
Aug. 30 2006-Sept. 3 2006
Abstract :
This paper presents a real-time electro-encephalogram (EEG) identification system with the goal of achieving hands free control. With two EEG electrodes placed on the scalp of the user, EEG signals are amplified and digitised directly using a ProComp+ encoder and transferred to the host computer through the RS232 interface. Using a real-time multilayer neural network, the actual classification for the control of a powered wheelchair has a very fast response. It can detect changes in the user´s thought pattern in 1 second. Using only two EEG electrodes at positions O1 and C4 the system can classify three mental commands (forward, left and right) with an accuracy of more than 79 %
Keywords :
biomedical electrodes; electroencephalography; encoding; handicapped aids; medical control systems; medical signal processing; neural nets; orthotics; pattern classification; peripheral interfaces; signal classification; ProComp+ encoder; RS232 interface; hands free control; mental commands; pattern classifier; powered wheelchair control; real-time electro-encephalogram identification system; real-time multilayer neural network; two-channel EEG electrodes; Cities and towns; Control systems; Electrodes; Electroencephalography; Embedded system; Multi-layer neural network; Portable computers; Real time systems; USA Councils; Wheelchairs;
Conference_Titel :
Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
Conference_Location :
New York, NY
Print_ISBN :
1-4244-0032-5
Electronic_ISBN :
1557-170X
DOI :
10.1109/IEMBS.2006.260157
