These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Spatio-temporal modeling for dense array ERP classification.
    Author: Kota S, Gupta L, Molfese D, Vaidyanathan R.
    Journal: Annu Int Conf IEEE Eng Med Biol Soc; 2008; 2008():2091-4. PubMed ID: 19163108.
    Abstract:
    A new strategy is introduced to exploit the enhanced spatial resolution offered by dense electrode arrays and to solve the dimensionality problem that plagues the design and evaluation of practical dense array event-related potential (ERP) classifiers. A spatio-temporal model is introduced to observe the dense array ERP amplitude variations across channels and time, simultaneously. Dimensionality reduction is achieved by selecting elements of the spatio-temporal arrays which differ in their probability distributions across the brain activity classes. Each selected spatio-temporal element is classified using an univariate Gaussian classifier and the resulting decisions are fused into a decision fusion vector which is classified using a discrete Bayes vector classifier. Using ERPs from a Stroop color test, it is shown that the performance improves significantly when the strategy is applied to normalized spatio-temporal ERP arrays. The main advantage of the new strategy is that it is not constrained by the dimensionality of the ERP vector. Consequently, it can be used to design ERP classifiers specialized for individual test subjects without having to collect a large number of ERPs from groups of subjects in order to solve the dimensionality problem.
    [Abstract] [Full Text] [Related] [New Search]