Comparison between ICA and wavelet-based denoising of single-trial evoked potentials

• Published in: Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.) Vol. 1; pp. 87 - 90
• Main Authors: Zouridakis, G., Iyer, D.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 2004
• Subjects: Analytical models; Blind source separation; Brain modeling; Computer science; Denoising; EEG; Electroencephalography; Evoked Potentials; Independent component analysis; Iterative methods; Noise reduction; Scalp; Wavelet analysis; Wavelets
• ISBN: 9780780384392; 0780384393
• ISSN: 1557-170X
• DOI: 10.1109/IEMBS.2004.1403097

Analysis of evoked potentials (EPs) on a single-trial basis allows the study of the dynamical characteristics of brain activity. However, single-trial responses are buried into the more prominent ongoing electroencephalographic (EEG) activity, and thus advanced procedures are needed to obtain the activity only of the cortical generators that are activated by the experimental task under study. We compare the effectiveness of two methods at removing extraneous activity from single-trial EPs, namely, a recently-proposed iterative procedure based on independent component analysis (ICA) and wavelet denoising, using simulated data and actual EP recordings from normal subjects, more specifically the auditory N100-P200 complex.