Two channel EEG thought pattern classifier.
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 O(1) and C(4) the system can classify three mental commands (forward, left and right) with an accuracy of more than 79 %
Craig
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DA
Key University Research Centre for Health Technologies, Faculty of Engineering, University of Technology, Sydney, NSW, Australia.
Nguyen
H T
HT
Burchey
H A
HA
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D007092:Imagination / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013647:Task Performance and Analysis; D013850:Thinking / Q000502:physiology*; D014584:User-Computer Interface