Literature DB >> 12814579

Stepping stone sampling for retrieving artifact-free electroencephalogram during functional magnetic resonance imaging.

Kimitaka Anami1, Takeyuki Mori, Fumiko Tanaka, Yusuke Kawagoe, Jun Okamoto, Masaru Yarita, Takashi Ohnishi, Masato Yumoto, Hiroshi Matsuda, Osamu Saitoh.   

Abstract

Ballistocardiogram and imaging artifacts cause major interference with simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) recording. In particular, the large amplitude of the imaging artifact precludes easy retrieval of EEG signals during fMRI scanning. Recording with 20,000-Hz digitization rate combined with 3000-Hz low-pass filter revealed the real waveform of the imaging artifact, in which it was elucidated that each artifact peak precisely corresponded to each gradient component and actually had differential waveforms of the original gradient pulses. Based on this finding, to retrieve EEG signal during fMRI acquisition, a blip-type echo planar sequence was modified so that EEG sampling might be performed at every 1000 micros (digitization rate 1000 Hz) exclusively in the period in which the artifact resided around the baseline level. This method, called "stepping stone sampling," substantially attenuated the amplitude of the imaging artifact. The remnant of the artifact was subtracted from the averaged artifact waveform. In human studies, alpha activity was successfully retrieved by inspection, and its attenuation/augmentation was observed during eyes open/closed periods. Fast Fourier transform analysis further revealed that even from DC up to 120 Hz, retrieved EEG data during scanning had very similar power distributions to the data retrieved during no scanning, implying the availability of the high-frequency band of the retrieved EEG signals, including even the gamma band.

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Year:  2003        PMID: 12814579     DOI: 10.1016/s1053-8119(03)00048-x

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  18 in total

1.  Influence of dense-array EEG cap on fMRI signal.

Authors:  Qingfei Luo; Gary H Glover
Journal:  Magn Reson Med       Date:  2011-12-09       Impact factor: 4.668

2.  Visual evoked potentials recovered from fMRI scan periods.

Authors:  Robert Becker; Petra Ritter; Matthias Moosmann; Arno Villringer
Journal:  Hum Brain Mapp       Date:  2005-11       Impact factor: 5.038

3.  Origin of the radio frequency pulse artifact in simultaneous EEG-fMRI recording: rectification at the carbon-metal interface.

Authors:  Michiro Negishi; Boris I Pinus; Alexander B Pinus; R Todd Constable
Journal:  IEEE Trans Biomed Eng       Date:  2007-09       Impact factor: 4.538

4.  Statistical feature extraction for artifact removal from concurrent fMRI-EEG recordings.

Authors:  Zhongming Liu; Jacco A de Zwart; Peter van Gelderen; Li-Wei Kuo; Jeff H Duyn
Journal:  Neuroimage       Date:  2011-10-20       Impact factor: 6.556

5.  Reducing the gradient artefact in simultaneous EEG-fMRI by adjusting the subject's axial position.

Authors:  Karen J Mullinger; Winston X Yan; Richard Bowtell
Journal:  Neuroimage       Date:  2010-10-13       Impact factor: 6.556

6.  An EEG (electroencephalogram) recording system with carbon wire electrodes for simultaneous EEG-fMRI (functional magnetic resonance imaging) recording.

Authors:  Michiro Negishi; Mark Abildgaard; Ilan Laufer; Terry Nixon; Robert Todd Constable
Journal:  J Neurosci Methods       Date:  2008-06-07       Impact factor: 2.390

7.  An open-source hardware and software system for acquisition and real-time processing of electrophysiology during high field MRI.

Authors:  Patrick L Purdon; Hernan Millan; Peter L Fuller; Giorgio Bonmassar
Journal:  J Neurosci Methods       Date:  2008-08-05       Impact factor: 2.390

8.  Single-trial discrimination for integrating simultaneous EEG and fMRI: identifying cortical areas contributing to trial-to-trial variability in the auditory oddball task.

Authors:  Robin I Goldman; Cheng-Yu Wei; Marios G Philiastides; Adam D Gerson; David Friedman; Truman R Brown; Paul Sajda
Journal:  Neuroimage       Date:  2009-04-02       Impact factor: 6.556

Review 9.  Epileptic networks studied with EEG-fMRI.

Authors:  Jean Gotman
Journal:  Epilepsia       Date:  2008       Impact factor: 5.864

10.  Adaptive and Wireless Recordings of Electrophysiological Signals During Concurrent Magnetic Resonance Imaging.

Authors:  Ranajay Mandal; Nishant Babaria
Journal:  IEEE Trans Biomed Eng       Date:  2018-10-23       Impact factor: 4.538

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