Classification of P300 component using a riemannian ensemble approach

Krzeminski, Dominik, Michelmann, Sebastian, Treder, Matthias ORCID: https://orcid.org/0000-0001-5955-2326 and Santamaria, Lorena 2019. Classification of P300 component using a riemannian ensemble approach. Presented at: MEDICON 2019XV Mediterranean Conference on Medical and Biological Engineering and Computing, Coimbra, Portugal, 26-28 September 2019. Ifmbe Proceedings. , vol.76 Springer Science Business Media, 10.1007/978-3-030-31635-8_229

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Abstract

We present a framework for P300 ERP classification on the 2019 IFMBE competition dataset using a combination of a Riemannian geometry and ensemble learning. Covariance matrices and ERP prototypes are extracted after the EEG is passed through a filter bank and an ensemble of LDA classifiers is trained on subsets of channels, trials, and frequencies. The model selects a final class based on maximum probability of evidence from all ensembles. Our pipeline achieves an average classification accuracy of 81.2% on the test set.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Psychology
Publisher:	Springer Science Business Media
ISSN:	1680-0737
Date of First Compliant Deposit:	1 October 2019
Date of Acceptance:	6 July 2019
Last Modified:	26 Oct 2022 07:46
URI:	https://orca.cardiff.ac.uk/id/eprint/125778

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