Literature DB >> 17282383

Removal of Ocular Artifacts from EEG: A Comparison of Adaptive Filtering Method and Regression Method Using Simulated Data.

P He1, M Kahle, G Wilson, C Russell.   

Abstract

We recently proposed an adaptive filtering method for removing ocular artifacts from EEG recordings. In this study, the accuracy of this method is evaluated quantitatively using simulated data and compared with the accuracy of the time domain regression method. The results show that when transfer of ocular signal to EEG channel is frequency dependent, or when there is a time delay, the adaptive filtering method is more accurate in recovering the true EEG signals.

Year:  2005        PMID: 17282383     DOI: 10.1109/IEMBS.2005.1616614

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  2 in total

1.  Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.

Authors:  Yang Nan; Javier Del Ser; Simon Walsh; Carola Schönlieb; Michael Roberts; Ian Selby; Kit Howard; John Owen; Jon Neville; Julien Guiot; Benoit Ernst; Ana Pastor; Angel Alberich-Bayarri; Marion I Menzel; Sean Walsh; Wim Vos; Nina Flerin; Jean-Paul Charbonnier; Eva van Rikxoort; Avishek Chatterjee; Henry Woodruff; Philippe Lambin; Leonor Cerdá-Alberich; Luis Martí-Bonmatí; Francisco Herrera; Guang Yang
Journal:  Inf Fusion       Date:  2022-06       Impact factor: 17.564

2.  Channel selection and feature projection for cognitive load estimation using ambulatory EEG.

Authors:  Tian Lan; Deniz Erdogmus; Andre Adami; Santosh Mathan; Misha Pavel
Journal:  Comput Intell Neurosci       Date:  2007
  2 in total

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