From Euclidean to Riemannian Means Information Geometry for SSVEP Classification - Emmanuel Kalunga, Eric Monacelli, Karim Djouani, Quentin Barthélemy, Sylvain Chevallier, Yskandar Hamam

[pcardosi]

Authors : Emmanuel Kalunga, Eric Monacelli, Karim Djouani, Quentin Barthélemy, Sylvain Chevallier, Yskandar Hamam
DOI URL : http://dx.doi.org/10.1007/978-3-319-25040-3_64
Video : http://www.youtube.com/watch?v=5HgNR1UWbaw
Slides: https://drive.google.com/open?id=0B0QKxsVtOaTiQjZfR0lkQmdwRjQ
Presentation : https://www.see.asso.fr/node/14265
Creative Commons Attribution-ShareAlike 4.0 International

Abstract:
Brain Computer Interfaces (BCI) based on electroencephalography (EEG) rely on multichannel brain signal processing. Most of the state-of-the-art approaches deal with covariance matrices, and indeed Riemannian geometry has provided a substantial framework for developing new algorithms. Most notably, a straightforward algorithm such as Minimum Distance to Mean yields competitive results when applied with a Riemannian distance. This applicative contribution aims at assessing the impact of several distances on real EEG dataset, as the invariances embedded in those distances have an influence on the classification accuracy. Euclidean and Riemannian distances and means are compared both in term of quality of results and of computational load.