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Classification of P300 component using a riemannian ensemble approach

Krzeminski, Dominik, Michelmann, Sebastian, Treder, Matthias 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. 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: 25 Sep 2020 02:05
URI: http://orca.cf.ac.uk/id/eprint/125778

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