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Wavelet denoising for P300 single-trial detection

Carolina Saavedra 1, * Laurent Bougrain 1
* Corresponding author
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Template-based analysis techniques are good candidates to robustly detect transient temporal graphic elements (e.g. event-related potential, k-complex, sleep spindles, vertex waves, spikes) in noisy and multi-sources electro-encephalographic signals. More specifically, we present the impact on a large dataset of a wavelet denoising to detect evoked potentials in a single-trial P300 speller. Using coiflets as a denoising process allows to obtain more stable accurracies for all subjects.
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https://hal.inria.fr/inria-00549218
Contributor : Carolina Saavedra <>
Submitted on : Tuesday, December 21, 2010 - 4:17:11 PM
Last modification on : Monday, April 19, 2021 - 5:30:06 PM
Long-term archiving on: : Tuesday, March 22, 2011 - 3:01:20 AM

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Carolina Saavedra, Laurent Bougrain. Wavelet denoising for P300 single-trial detection. Proccedings of the 5th french conference on computational neuroscience - Neurocomp'10, Oct 2010, Lyon, France. pp.227-231. ⟨inria-00549218⟩

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