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Conference Papers Year : 2016

Modeling time warping in tensor decomposition

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—Taking into account subject variability in data mining is one of the great challenges of modern biomedical engineering. In EEG recordings, the assumption that time sources are exactly shared by multiple subjects, multiple recordings of the same subject, or even multiples instances of the sources in one recording is especially wrong. In this paper, we propose to deal with shared underlying sources expressed through time warping in multiple EEG recordings, in the context of ocular artifact removal. Diffeomorphisms are used to model the time warping operators. We derive an algorithm that extracts all sources and diffeomorphism in the model and show successful simulations, giving a proof of concept that subject variability can be tackled with tensor modeling.
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hal-01400552 , version 1 (23-07-2018)



Bertrand Rivet, Jeremy E Cohen. Modeling time warping in tensor decomposition. SAM 2016 - 9th IEEE Sensor Array and Multichannel Signal Processing Workshop, Jul 2016, Rio de Janeiro, Brazil. pp.1 - 5, ⟨10.1109/SAM.2016.7569733⟩. ⟨hal-01400552⟩
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