research-article

A direct optimization approach to the P300 speller

Authors:

Roberto Santana,

Santiago Muelas,

Antonio LaTorre,

Jose M. PeñaAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 1747 - 1754

https://doi.org/10.1145/2001576.2001811

Published: 12 July 2011 Publication History

Abstract

The P300 component of the brain event-related-potential is one of the most used signals in brain computer interfaces (BCIs). One of the required steps for the application of the P300 paradigm is the identification of this component in the presence of stimuli. In this paper we propose a direct optimization approach to the P300 classification problem. A general formulation of the problem is introduced. Different classes of optimization algorithms are applied to solve the problem and the concepts of k-best and k-worst ensembles of solutions are introduced as a way to improve the accuracy of single solutions. The introduced approaches are able to achieve a classification rate over 80% on test data.

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Cited By

Santana RBonnet LLegény JLécuyer AMoore J(2012)Introducing the use of model-based evolutionary algorithms for EEG-based motor imagery classificationProceedings of the 14th annual conference on Genetic and evolutionary computation10.1145/2330163.2330323(1159-1166)Online publication date: 7-Jul-2012
https://dl.acm.org/doi/10.1145/2330163.2330323
LaTorre AMuelas SPena J(2011)Benchmarking a hybrid DE-RHC algorithm on real world problems2011 IEEE Congress of Evolutionary Computation (CEC)10.1109/CEC.2011.5949730(1027-1033)Online publication date: Jun-2011
https://doi.org/10.1109/CEC.2011.5949730

Index Terms

A direct optimization approach to the P300 speller
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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Published In

cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 12 July 2011

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GECCO '11

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GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Cited By

Santana RBonnet LLegény JLécuyer AMoore J(2012)Introducing the use of model-based evolutionary algorithms for EEG-based motor imagery classificationProceedings of the 14th annual conference on Genetic and evolutionary computation10.1145/2330163.2330323(1159-1166)Online publication date: 7-Jul-2012
https://dl.acm.org/doi/10.1145/2330163.2330323
LaTorre AMuelas SPena J(2011)Benchmarking a hybrid DE-RHC algorithm on real world problems2011 IEEE Congress of Evolutionary Computation (CEC)10.1109/CEC.2011.5949730(1027-1033)Online publication date: Jun-2011
https://doi.org/10.1109/CEC.2011.5949730

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