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Time adaptive denoising of single trial event‐ related potentials in the wavelet domain
Author(s) -
Effern Arndt,
Lehnertz Klaus,
Grunwald Thomas,
Fernández Guillén,
David Peter,
Elger Christian E.
Publication year - 2000
Publication title -
psychophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.661
H-Index - 156
eISSN - 1469-8986
pISSN - 0048-5772
DOI - 10.1111/1469-8986.3760859
Subject(s) - wavelet , filter (signal processing) , psychology , noise reduction , event related potential , white noise , wiener filter , adaptive filter , noise (video) , pattern recognition (psychology) , time domain , speech recognition , artificial intelligence , algorithm , statistics , computer science , electroencephalography , mathematics , cognitive psychology , computer vision , neuroscience , image (mathematics)
We present a new wavelet‐based method for single trial analysis of transient and time variant event‐related potentials (ERPs). Expecting more accurate filter settings than achieved by other techniques (low‐pass filter, a posteriori Wiener filter, time invariant wavelet filter), ERPs were initially balanced in time. By simulation, better filter performance could be established for test signals contaminated with either white noise or isospectral noise. To provide an example of real application, the method was applied to limbic P300 potentials (MTL‐P300). As a result, variance of single trial MTL‐P300s decreased, without restricting the corresponding mean. The proposed method can be regarded as an alternative for single‐trial ERP analysis.