Krzeminski, Dominik, Michelmann, Sebastian, Treder, Matthias ![]() |
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Official URL: http://dx.doi.org/10.1007/978-3-030-31635-8_229
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) |
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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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