Improved (I)CA-noise elimination of electrophysiological data using band-pass filtered components

Abstract

Event Abstract Back to Event Improved (I)CA-noise elimination of electrophysiological data using band-pass filtered components Removal of signal components unrelated to the signals of interest, so called 'noise', is a general problem in many types of data. One method for identifying and removing noise (e.g. eye movement artifacts or power-line noise) from electrophysiological data is independent component analysis (ICA) (e.g. Jung et al., 1998). A shortcoming of this method is that it does not guarantee a complete separation of physiological signals and noise sources into different components. Thus, those components containing obvious noise contributions might also contain physiological data. This phenomenon is called 'leakage'. Rejecting these components might therefore not only clean the noise but may additionally and erroneously take out parts of the physiological data. Here, we suggest that this leakage can be reduced using prior knowledge about the spectral features of the noise. After identification of the noise components, we applied band-pass filtering to these components at the frequencies of the noise before subtracting the back-projected components from the data. This effectively prevents deletion of physiological signals in frequency bands beyond the a priori specified noise frequency bands. To test this method, we applied it to simulated data and physiological signals obtained from ECoG recordings in macaque monkey. Our results demonstrate that this method (a) is as efficient as the standard ICA correction, (b) also works for data available only as short time segments ('trials'), (c) results in a clearer noise time-course allowing the subtraction of less non-noise activity, and (d) does not - in contrast to the standard unfiltered ICA method - change the power spectra of the cleared signals in spectral regions outside the noise regions. Additionally, we present a simple and computationally efficient method to find noise components with sharp frequency tunings (e.g. line noise), which can be used independently of or prior to ICA to improve its performance. To achieve this, we band-pass filter the original signal prior to component identification (which can consequently be done using standard component analysis techniques, e.g. PCA/ICA). We thereby reduce the influence of components whose main contribution lies in other frequency bands, thereby rendering the noise components more salient to the component identification algorithm. This method is especially useful when no clear noise-components can be detected using standard ICA on the unfiltered data. In summary, we show that ICA noise elimination is improved using prior knowledge about the spectral features of the noise, ultimately enhancing the signal-to-noise ratio of neurophysiological data recordings. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). Improved (I)CA-noise elimination of electrophysiological data using band-pass filtered components. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.257 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 03 Feb 2009; Published Online: 03 Feb 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract Supplemental Data The Authors in Frontiers Google Google Scholar PubMed Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.