DocumentCode :
1195144
Title :
A wavelet-like filter based on neuron action potentials for analysis of human scalp electroencephalographs
Author :
Glassman, Elena L.
Author_Institution :
Massachusetts Inst. of Technol., Cambridge, MA, USA
Volume :
52
Issue :
11
fYear :
2005
Firstpage :
1851
Lastpage :
1862
Abstract :
This paper describes the development and testing of a wavelet-like filter, named the SNAP, created from a neural activity simulation and used, in place of a wavelet, in a wavelet transform for improving EEG wavelet analysis, intended for brain-computer interfaces. The hypothesis is that an optimal wavelet can be approximated by deriving it from underlying components of the EEG. The SNAP was compared to standard wavelets by measuring Support Vector Machine-based EEG classification accuracy when using different wavelets/filters for EEG analysis. When classifying P300 evoked potentials, the error, as a function of the wavelet/filter used, ranged from 6.92% to 11.99%, almost twofold. Classification using the SNAP was more accurate than that with any of the six standard wavelets tested. Similarly, when differentiating between preparation for left- or right-hand movements, classification using the SNAP was more accurate (10.03% error) than for four out of five of the standard wavelets (9.54% to 12.00% error) and internationally competitive (7% error) on the 2001 NIPS competition test set. Phenomena shown only in maps of discriminatory EEG activity may explain why the SNAP appears to have promise for improving EEG wavelet analysis. It represents the initial exploration of a potential family of EEG-specific wavelets.
Keywords :
bioelectric potentials; biomechanics; electroencephalography; filters; handicapped aids; medical signal processing; neurophysiology; signal classification; support vector machines; wavelet transforms; EEG classification; P300 evoked potentials; brain-computer interfaces; hand movements; human scalp electroencephalographs; neural activity simulation; neuron action potentials; support vector machine; wavelet transform; wavelet-like filter; Analytical models; Brain modeling; Electroencephalography; Filters; Humans; Neurons; Scalp; Testing; Wavelet analysis; Wavelet transforms; Brain-computer interface (BCI); data-specific wavelets; electroencephalography (EEG); pattern classification; pattern recognition; time-frequency representations; wavelet analysis; Action Potentials; Algorithms; Artificial Intelligence; Brain; Computer Simulation; Diagnosis, Computer-Assisted; Electroencephalography; Event-Related Potentials, P300; Humans; Models, Neurological; Neurons; Pattern Recognition, Automated; Scalp; Signal Processing, Computer-Assisted; Therapy, Computer-Assisted; User-Computer Interface;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/TBME.2005.856277
Filename :
1519594
Link To Document :
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