Hysteresis thresholding for Wavelet denosing applied to P300 single-trial detection

Carolina Saavedra (1) , Laurent Bougrain (1) , Radu Ranta (2)

1 CORTEX - Neuromimetic intelligence
2 CRAN - Centre de Recherche en Automatique de Nancy

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 significant impact on a large dataset of wavelet denoisings to detect evoked potentials in a single-trial P300 speller. We apply the classical thresholds selection rules algorithms and compare them with the hysteresis algorithm presented in \cite{Ranta10hyst} which combine the classical thresholds to detect blocks of significant wavelets coefficients based on the graph structure of the wavelet decomposition.

Domaines: Neurosciences

Carolina Saavedra, Laurent Bougrain, Radu Ranta. Hysteresis thresholding for Wavelet denosing applied to P300 single-trial detection. [Research Report] RR-7723, IPS, INRIA. 2011, pp.5. ⟨inria-00618694v2⟩