research-article

Learning to recognise mental activities: genetic programming of stateful classifiers for brain-computer interfacing

Authors:

Alexandros Agapitos,

Matthew Dyson,

Simon M. Lucas,

Francisco SepulvedaAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 1155 - 1162

https://doi.org/10.1145/1389095.1389326

Published: 12 July 2008 Publication History

Get Access

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Learning to recognise mental activities: genetic programming of stateful classifiers for brain-computer interfacing

Pages 1155 - 1162

Abstract
References

Abstract

Two families (stateful and stateless) of genetically programmed classifiers were tested on a five class brain-computer interface (BCI) data set of raw EEG signals. The ability of evolved classifiers to discriminate mental tasks from each other were analysed in terms of accuracy, precision and recall. A model describing the dynamics of state usage in stateful programs is introduced. An investigation of relationships between the model attributes and associated classification results was made. The results show that both stateful and stateless programs can be successfully evolved for this task, though stateful programs start from lower fitness and take longer to evolve.

References

[1]

A. Agapitos and S. M. Lucas. Evolving a statistics class using object oriented evolutionary programming. In Proc. of 10th European Conference on Genetic Programming, volume 4445, pages 291--300, 2007.

Digital Library

Google Scholar

[2]

A. Agapitos, J. Togelius, and S. M. Lucas. Multiobjective techniques for the use of state in genetic programming applied to simulated car racing. In Proc. of IEEE CEC, pages 1562--1569, 2007.

Crossref

Google Scholar

[3]

E. Alfaro-Cid, A. Esparcia-Alcázar, and K. Sharman. A first attempt at constructing genetic programming expressions for EEG classification. In European Symposium on Artificial Neural Networks, pages 59--66, 2006.

Google Scholar

[4]

K. Chellapilla. Evolving computer programs without subtree crossover. IEEE Trans. Evolutionary Computation, 1(3):209--216, 1997.

Digital Library

Google Scholar

[5]

M. Conrads, P. Nordin, and W. Banzhaf. Speech sound discrimination with genetic programming. In Proc. of First European Workshop on Genetic Programming, volume 1391, pages 113--129, 1998.

Digital Library

Google Scholar

[6]

C. Estébanez, J. M. Valls, R. Aler, and I. M. Galván. A first attempt at constructing genetic programming expressions for EEG classification. In 15th International Conference of Artificial Neural Networks: Biological Inspirations, volume 3696, pages 665--670, 2005.

Digital Library

Google Scholar

[7]

J. Koza. Genetic Programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MA, (1992).

Digital Library

Google Scholar

[8]

W. B. Langdon. Evolving data structures with genetic programming. In L. J. Eshelman, editor, Proc. of the Sixth Int. Conf. on Genetic Algorithms, pages 295--302. Morgan Kaufmann, 1995.

Digital Library

Google Scholar

[9]

T. Loveard and V. Ciesielski. Representing classification problems in genetic programming. In Proc. of IEEE CEC, volume 2, pages 1070--1077, 2001.

Crossref

Google Scholar

[10]

R. Poli, C. Cinel, L. Citi, and F. Sepulveda. Evolutionary brain computer interfaces. In Proc. of EvoWorkshops, volume 4448, pages 301--310, 2007.

Digital Library

Google Scholar

[11]

W. Smart and M. Zhang. Probability based genetic programming for multiclass object classification. In 8th Pacific Rim International Conference on Artificial Intelligence, volume 3157, pages 251--261, 2004.

Digital Library

Google Scholar

[12]

A. Teller. Learning mental models. In Proceedings of the Fifth Workshop on Neural Networks, 1993.

Google Scholar

[13]

A. Teller and M. Veloso. Program evolution for data mining. The International Journal of Expert Systems, 8(3):216--236, 1995.

Google Scholar

Cited By

View all

Agapitos ABrabazon AO’Neill M(2017)Regularised gradient boosting for financial time-series modellingComputational Management Science10.1007/s10287-017-0280-y14:3(367-391)Online publication date: 23-May-2017
https://doi.org/10.1007/s10287-017-0280-y
Agapitos ABrabazon AO’Neill M(2016)Genetic Programming with Memory For Financial TradingApplications of Evolutionary Computation10.1007/978-3-319-31204-0_2(19-34)Online publication date: 15-Mar-2016
https://doi.org/10.1007/978-3-319-31204-0_2
Agapitos AO'Neill MBrabazon ALanzi P(2011)Stateful program representations for evolving technical trading rulesProceedings of the 13th annual conference companion on Genetic and evolutionary computation10.1145/2001858.2001969(199-200)Online publication date: 12-Jul-2011
https://dl.acm.org/doi/10.1145/2001858.2001969

Recommendations

Mental tasks-based brain-robot interface

This paper describes a Brain Computer Interface (BCI) based on electroencephalography (EEG) that allows control of a robot arm. This interface will enable people with severe disabilities to control a robot arm to assist them in a variety of tasks in ...
Electroencephalogram signals from imagined activities: a novel biometric identifier for a small population
IDEAL'06: Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning

Electroencephalogram (EEG) signals extracted during imagined activities have been studied for use in Brain Computer Interface (BCI) applications. The major hurdle in the EEG based BCI is that the EEG signals are unique to each individual. This ...
A hybrid ensemble voting-based residual attention network for motor imagery EEG Classification
Abstract
Multi-class motor imagery Electroencephalography (EEG) activity decoding has always been challenging for the development of Brain Computer Interface (BCI) system. Deep learning has recently emerged as a powerful approach for BCI system development ...

Comments

Information & Contributors

Information

Published In

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

GECCO08

Sponsor:

GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
226
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 08 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Agapitos ABrabazon AO’Neill M(2017)Regularised gradient boosting for financial time-series modellingComputational Management Science10.1007/s10287-017-0280-y14:3(367-391)Online publication date: 23-May-2017
https://doi.org/10.1007/s10287-017-0280-y
Agapitos ABrabazon AO’Neill M(2016)Genetic Programming with Memory For Financial TradingApplications of Evolutionary Computation10.1007/978-3-319-31204-0_2(19-34)Online publication date: 15-Mar-2016
https://doi.org/10.1007/978-3-319-31204-0_2
Agapitos AO'Neill MBrabazon ALanzi P(2011)Stateful program representations for evolving technical trading rulesProceedings of the 13th annual conference companion on Genetic and evolutionary computation10.1145/2001858.2001969(199-200)Online publication date: 12-Jul-2011
https://dl.acm.org/doi/10.1145/2001858.2001969

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Recommendations

Mental tasks-based brain-robot interface

Electroencephalogram signals from imagined activities: a novel biometric identifier for a small population

A hybrid ensemble voting-based residual attention network for motor imagery EEG Classification

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations