A Method for Removing Imaging Artifact from Continuous EEG Recorded during Functional MRI
Abstract
Combined EEG/fMRI recording has been used to localize the generators of EEG events and to identify subject state in cognitive studies and is of increasing interest. However, the large EEG artifacts induced during fMRI have precluded simultaneous EEG and fMRI recording, restricting study design. Removing this artifact is difficult, as it normally exceeds EEG significantly and contains components in the EEG frequency range. We have developed a recording system and an artifact reduction method that reduce this artifact effectively. The recording system has large dynamic range to capture both low-amplitude EEG and large imaging artifact without distortion (resolution 2 microV, range 33.3 mV), 5-kHz sampling, and low-pass filtering prior to the main gain stage. Imaging artifact is reduced by subtracting an averaged artifact waveform, followed by adaptive noise cancellation to reduce any residual artifact. This method was validated in recordings from five subjects using periodic and continuous fMRI sequences. Spectral analysis revealed differences of only 10 to 18% between EEG recorded in the scanner without fMRI and the corrected EEG. Ninety-nine percent of spike waves (median 74 microV) added to the recordings were identified in the corrected EEG compared to 12% in the uncorrected EEG. The median noise after artifact reduction was 8 microV. All these measures indicate that most of the artifact was removed, with minimal EEG distortion. Using this recording system and artifact reduction method, we have demonstrated that simultaneous EEG/fMRI studies are for the first time possible, extending the scope of EEG/fMRI studies considerably.
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Strong electromagnetic interference causes artifacts in simultaneously acquired fMRI and EEG signals, a critical challenge for integration.
“A critical challenge for integrating fMRI and EEG is the strong electromagnetic interference that causes artifacts in simultaneously acquired fMRI and EEG signals (178).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [178]
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- Authors
- Allen, P. J., Josephs, O., & Turner, R.
- Affiliations
- National Hospital for Neurology and Neurosurgery (GB)
- UCL Queen Square Institute of Neurology (GB)
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2000-08
- Volume
- 12
- Issue
- 2
- Pages
- 230-239
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2163254535
- Cited by
- 1,301
- References
- 18
- Citations by year
- 2026: 35 · 2025: 57 · 2024: 33 · 2023: 45 · 2022: 60 · 2021: 68 · 2020: 51 · 2019: 61 · 2018: 63 · 2017: 60 · 2016: 67 · 2015: 57 · 2014: 81 · 2013: 82 · 2012: 69
- FWCI
- 7.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Keywords
- EEG artifact removal, functional MRI, artifact reduction, adaptive noise cancellation, low-pass filtering, EEG noise
- MeSH terms
- Adult, Algorithms, Electroencephalography, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Signal Processing, Computer-Assisted, Reproducibility of Results, Artifacts
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