Self-Organizing Maps for Event-Related Potential Data Analysis

Lukáš Vařeka, Pavel Mautner

Abstract

Event-Related Potentials (ERPs) and especially the P300 component have been gaining attention in braincomputer interface design and neurobiological research. The detection of the P300 component in electroencephalographic signal is challenging since its signal-to-noise ratio is very low. Instead of using traditional supervised pattern recognition, this paper discusses using unsupervised neural networks for the P300 classification purposes. To validate the proposed approach, a method for the P300 detection based on matching pursuit and self-organizing maps is proposed and evaluated. The results may be applied to the design of brain-computer interfaces.

References

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Paper Citation


in Harvard Style

Vařeka L. and Mautner P. (2014). Self-Organizing Maps for Event-Related Potential Data Analysis . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014) ISBN 978-989-758-010-9, pages 387-392. DOI: 10.5220/0004885103870392


in Bibtex Style

@conference{healthinf14,
author={Lukáš Vařeka and Pavel Mautner},
title={Self-Organizing Maps for Event-Related Potential Data Analysis},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014)},
year={2014},
pages={387-392},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004885103870392},
isbn={978-989-758-010-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014)
TI - Self-Organizing Maps for Event-Related Potential Data Analysis
SN - 978-989-758-010-9
AU - Vařeka L.
AU - Mautner P.
PY - 2014
SP - 387
EP - 392
DO - 10.5220/0004885103870392