Riemannian classification analysis for model EEG intention speed patterns

Vicente Quiles; Laura Ferrero; Eduardo Ianez; Mario Ortiz; Jose M Azorin

doi:10.1109/EMBC48229.2022.9871561

Riemannian classification analysis for model EEG intention speed patterns

Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul:2022:402-405. doi: 10.1109/EMBC48229.2022.9871561.

Authors

Vicente Quiles, Laura Ferrero, Eduardo Ianez, Mario Ortiz, Jose M Azorin

PMID: 36086011
DOI: 10.1109/EMBC48229.2022.9871561

Abstract

In this paper, the paradigm of the intention of speed changes from EEG signals with Riemannian classifiers methods is studied in 10 subjects. In addition, the best frequency band and how different electrode configurations affect the accuracy of the model are analyzed. In the prediction of the intention to change speed, results of 68.6% were obtained, in the one of only Increase, results of 64.41 % were obtained, and in the one of only Decrease, results of 71.5% were obtained.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Electrodes
Electroencephalography* / methods
Humans
Intention*