Inductive means and sequences applied to online classification of EEG

Abstract : The translation of brain activity into user command, through Brain-Computer Interfaces (BCI), is a very active topic in machine learning and signal processing. As commercial applications and out-of-the-lab solutions are proposed, there is an increased pressure to provide on-line algorithms and real-time implementations. Electroencephalography (EEG) systems offer lightweight and wearable solutions, at the expense of signal quality. Approaches based on covariance matrices have demonstrated good robustness to noise and provide a suitable representation for classification tasks, relying on advances in Riemannian geometry. We propose to equip the minimum distance to mean (MDM) classifier with a new family of means, based on the inductive mean, for block-online classification tasks and to embed the inductive mean in an incremental learning algorithm for online classification of EEG.
Complete list of metadatas

Cited literature [11 references]  Display  Hide  Download

https://hal.uvsq.fr/hal-01711499
Contributor : Sylvain Chevallier <>
Submitted on : Sunday, February 18, 2018 - 11:54:20 AM
Last modification on : Monday, March 26, 2018 - 1:12:59 AM
Long-term archiving on : Monday, May 7, 2018 - 11:09:27 AM

File

31-Massart.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01711499, version 1

Collections

Citation

Estelle Massart, Sylvain Chevallier. Inductive means and sequences applied to online classification of EEG. 3rd International Conference on Geometric Science of Information (GSI), Nov 2017, Paris, France. ⟨hal-01711499⟩

Share

Metrics

Record views

92

Files downloads

159