NDLI: A self-paced BCI using stationary wavelet packets

Content Provider	IEEE Xplore Digital Library
Author	Faradji, F. Ward, R.K. Birch, G.E.
Copyright Year	2009
Description	Author affiliation: Electrical and Computer Engineering Department, University of British Columbia, Vancouver, BC V6T 1Z4 Canada (Faradji, F.; Ward, R.K.) \|\| Electrical and Computer Engineering Department, University of British Columbia, Vancouver, BC V6T 1Z4 Canada. He is also with the Neil Squire Society, Burnaby, BC V5M 3Z3 Canada (Birch, G.E.)
Abstract	The stationary wavelet packet analysis is exploited for the first time in the design of a self-paced BCI based on mental tasks. The BCI system is custom designed to achieve a zero false positive rate, as false activations highly restricts the applications of BCIs in real life. The EEG signals of four subjects performing five different mental tasks are used as the dataset. The stationary wavelet packets decompose the signal into eight components. The features used are the autoregressive coefficients obtained by applying autoregressive modeling on the resultant wavelet components. Classification is a two-stage process. The first stage is based on quadratic discriminant analysis which is extremely fast. The second stage is a simple majority voting classifier. During model selection, which is performed via 5-folded cross-validation, the combination of decomposed components and the autoregressive model order that yield the best performance are selected. Results show enhancements in the overall performance for three subjects comparing to our previously designed BCI.
Starting Page	962
Ending Page	965
File Size	399350
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5332802
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet packets Electroencephalography Brain modeling Electrodes Wavelet analysis Frequency USA Councils Voting Sampling methods Impedance
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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