Wavelet denoising for P300 single-trial detection

Carolina Saavedra (1) , Laurent Bougrain (1)

1 CORTEX - Neuromimetic intelligence

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.

Mots clés: event-related potential; wavelet denoising; support vector machines; brain-computer interface

Domaines: Apprentissage [cs.LG]; Bio-informatique [q-bio.QM]; Bio-Informatique, Biologie Systémique [q-bio.QM]

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⟩