Toward Fewer EEG Channels and Better Feature Extractor of Non-Motor Imagery Mental Tasks Classification for a Wheelchair Thought Controller

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2012), 2012, pp. 5266 - 5269
Issue Date:
2012-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2011006477OK.pdf4.64 MB
Adobe PDF
This paper presents a non-motor imagery tasks classification electroencephalography (EEG) based brain computer interface (BCI) for wheelchair control. It uses only two EEG channels and a better feature extractor to improve the portability and accuracy in the practical system. In addition, two different features extraction methods, power spectral density (PSD) and Hilbert Huang Transform (HHT) energy are compared to find a better method with improved classification accuracy using a Genetic Algorithm (GA) based neural network classifier. The results from five subjects show that using the original eight channels with three tasks, accuracy between 76% and 85% is achieved. With only two channels in combination with the best chosen task using a PSD feature extractor, the accuracy is reduced to between 65% and 79%. However, the HHT based method provides an improved accuracy between 70% and 84% for the classification of three discriminative tasks using two EEG channels.
Please use this identifier to cite or link to this item: