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Furthermore, the temporal resolution of the data is left unaltered, as it would occur in many common data-smoothing methods. The design of this filter has been influenced by improving the calculation accuracy of dynamical measures, such as fractal dimensions and Lyapunov exponents, of neurodynamical recordings such as those obtained through electroencephalography (EEG) or magnetoencephalography (MEG).<\/jats:p>","DOI":"10.1142\/s179353691250015x","type":"journal-article","created":{"date-parts":[[2012,8,31]],"date-time":"2012-08-31T09:17:51Z","timestamp":1346404671000},"page":"1250015","source":"Crossref","is-referenced-by-count":1,"title":["USING EMPIRICAL MODE DECOMPOSITION WEIGHTING FUNCTIONALS AS TIME SERIES FILTERING COEFFICIENTS FOR PHYSIOLOGICAL RECORDINGS"],"prefix":"10.1142","volume":"04","author":[{"given":"JOHN L.","family":"AVEN","sequence":"first","affiliation":[{"name":"ViaLogy, 283 South Lake Avenue, Suite 205, Pasadena, California 91101, USA"}]},{"given":"ARNOLD J.","family":"MANDELL","sequence":"additional","affiliation":[{"name":"Cielo Institute, 486 Sunset Dr., Asheville, North Carolina 28804-3727, USA"}]},{"given":"RICHARD","family":"COPPOLA","sequence":"additional","affiliation":[{"name":"NIMH MEG Core Facility, National Institutes of Health, Building 10, Room 3C119, Bethesda, Maryland 20892, USA"}]}],"member":"219","published-online":{"date-parts":[[2012,8,31]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2005.06.011"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2008.04.023"},{"key":"rf4","doi-asserted-by":"crossref","first-page":"495","DOI":"10.55782\/ane-2000-1369","volume":"60","author":"Bhattacharya J.","journal-title":"Acta Neurobiol. 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