Inductive Means and Sequences Applied to Online Classification of EEG

Massart, Estelle;Chevallier, Sylvain
(2017) International Conference on Geometric Science of Information(GSI 2017)

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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 online 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.
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Massart, E., & Chevallier, S. (2017). Inductive Means and Sequences Applied to Online Classification of EEG. Lecture Notes in Computer Science : Geometric Science of Information, p. 763-770. https://doi.org/10.1007/978-3-319-68445-1_88