Adaptive EEG thought pattern classifier for advanced wheelchair control.
This paper presents a real-time Electroencephalogram (EEG) classification system, with the goal of enhancing the control of a head-movement controlled power wheelchair for patients with chronic Spinal Cord Injury (SCI). Using a 32 channel recording device, mental command data was collected from 10 participants. This data was used to classify three different mental commands, to supplement the five commands already available using head movement control. Of the 32 channels that were recorded only 4 were used in the classification, achieving an average classification rate of 82%. This paper also demonstrates that there is an advantage to be gained by doing adaptive training of the classifier. That is, customizing the classifier to a person previously unseen by the classifier caused their average recognition rates to improve from 52.5% up to 77.5%.
Craig
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DA
Faculty of Engineering, University of Technology, Sydney, NSW, Australia. daniel.craig@eng.uts.edu.au
Nguyen
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HT
D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D013119:Spinal Cord Injuries; D014584:User-Computer Interface; D014910:Wheelchairs