Deep Learning Techniques for Classification of P300 Component

Jiří Vaněk, Roman Mouček

2018

Abstract

Deep learning techniques have proved to be beneficial in many scientific disciplines and have beaten stateof- the-art approaches in many applications. The main aim of this article is to improve the success rate of deep learning algorithms, especially stacked autoencoders, when they are used for detection and classification of P300 event-related potential component that reflects brain processes related to stimulus evaluation or categorization. Moreover, the classification results provided by stacked autoencoders are compared with the classification results given by other classification models and classification results provided by combinations of various types of neural network layers.

Paper Citation

in Harvard Style

Vaněk J. and Mouček R. (2018). Deep Learning Techniques for Classification of P300 Component. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF; ISBN 978-989-758-281-3, SciTePress, pages 446-453. DOI: 10.5220/0006594104460453

in Bibtex Style

@conference{healthinf18,
author={Jiří Vaněk and Roman Mouček},
title={Deep Learning Techniques for Classification of P300 Component},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF},
year={2018},
pages={446-453},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006594104460453},
isbn={978-989-758-281-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF
TI - Deep Learning Techniques for Classification of P300 Component
SN - 978-989-758-281-3
AU - Vaněk J.
AU - Mouček R.
PY - 2018
SP - 446
EP - 453
DO - 10.5220/0006594104460453
PB - SciTePress