Document Detail


Convolutional neural networks for P300 detection with application to brain-computer interfaces.
MedLine Citation:
PMID:  20567055     Owner:  NLM     Status:  In-Process    
Abstract/OtherAbstract:
A Brain-Computer Interface (BCI) is a specific type of human-computer interface that enables the direct communication between human and computers by analyzing brain measurements. Oddball paradigms are used in BCI to generate event-related potentials (ERPs), like the P300 wave, on targets selected by the user. A P300 speller is based on this principle, where the detection of P300 waves allows the user to write characters. The P300 speller is composed of two classification problems. The first classification is to detect the presence of a P300 in the electroencephalogram (EEG). The second one corresponds to the combination of different P300 responses for determining the right character to spell. A new method for the detection of P300 waves is presented. This model is based on a convolutional neural network (CNN). The topology of the network is adapted to the detection of P300 waves in the time domain. Seven classifiers based on the CNN are proposed: four single classifiers with different features set and three multiclassifiers. These models are tested and compared on the Data set II of the third BCI competition. The best result is obtained with a multiclassifier solution with a recognition rate of 95.5 percent, without channel selection before the classification. The proposed approach provides also a new way for analyzing brain activities due to the receptive field of the CNN models.
Authors:
Hubert Cecotti; Axel Gräser
Related Documents :
10179865 - Medical devices; classification/reclassification of immunohistochemistry reagents and k...
12499295 - Simple rules underlying gene expression profiles of more than six subtypes of acute lym...
16529955 - Fmri pattern classification using neuroanatomically constrained boosting.
15961495 - Bayesian neural network approaches to ovarian cancer identification from high-resolutio...
24386325 - Bayes and empirical bayes estimators of abundance and density from spatial capture-reca...
9308535 - Single bone straight line graphs for the lower extremity.
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  IEEE transactions on pattern analysis and machine intelligence     Volume:  33     ISSN:  1939-3539     ISO Abbreviation:  IEEE Trans Pattern Anal Mach Intell     Publication Date:  2011 Mar 
Date Detail:
Created Date:  2011-04-21     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9885960     Medline TA:  IEEE Trans Pattern Anal Mach Intell     Country:  United States    
Other Details:
Languages:  eng     Pagination:  433-45     Citation Subset:  IM    
Affiliation:
Institute of Automation, University of Bremen, Otto-Hahn-Allee, Germany. hcecotti@orange.fr
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  A Discourse on the Contributions of Evidence-based Medicine to Wound Care.
Next Document:  Parallel Iteration to the Radiative Transport in Inhomogeneous Media with Bootstrapping.