Adaptive EEG thought pattern classifier for advanced wheelchair control

Publication Type:
Conference Proceeding
Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 2007, pp. 2544 - 2547
Issue Date:
Filename Description Size
Thumbnail2007000027.pdf408.41 kB
Adobe PDF
Full metadata record
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%. © 2007 IEEE.
Please use this identifier to cite or link to this item: