Improving the quality of the electroencephalogram simultaneously recorded with functional magnetic resonance imaging

David Steyrl

Research output: ThesisDoctoral ThesisResearch

Abstract

The concurrent recording of the electroencephalogram (EEG) with functional magnetic resonance imaging (fMRI) allows the simultaneous study of the electrophysiology, the blood oxygen level dependent signal, and particularly also their interplay. However, the EEG is affected by a large number of fMRI-related, partly repetitive, artifacts. Average artifact subtraction (AAS) – the most frequently used artifact reduction technique – computes artifact templates from artifact repetitions and subtracts them from the EEG. This effectively reduces repetitive, invariant artifacts, but serious artifact residuals remain. Therefore, this thesis pursued two objectives: analysis of the artifact residuals and development of a new technique for the reduction of the residuals.
The inherent variability of artifacts is known to cause residuals after the AAS method, because the subtraction template does not fit the actual artifact. In this thesis, an additional cause of artifact residuals was identified. An intrinsic vulnerability of the AAS technique to correlated artifacts leads to artifact contaminated subtraction templates and consequently to artifact residuals in the EEG. The new artifact reduction technique uses recordings of artifact residuals from a reference-layer EEG cap combined with adaptive filtering to remove the residuals from the EEG and is referred to as reference-layer adaptive filtering (RLAF). The RLAF method is highly effective in offline and online application scenarios. It improves the signal-to-noiseratio as well as the classification accuracy of physiological EEG components substantially. The RLAF technique’s ability to reduce all kinds of artifact residuals – including non-stationary and varying components – in combination with its easy handling, makes it a candidate for a future gold standard method.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Graz University of Technology (90000)
Supervisors/Advisors
  • Müller-Putz, Gernot, Supervisor
Award date21 Nov 2018
Publication statusPublished - 21 Nov 2018

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Fields of Expertise

  • Human- & Biotechnology

Cite this

Improving the quality of the electroencephalogram simultaneously recorded with functional magnetic resonance imaging. / Steyrl, David.

2018. 140 p.

Research output: ThesisDoctoral ThesisResearch

Steyrl, D 2018, 'Improving the quality of the electroencephalogram simultaneously recorded with functional magnetic resonance imaging', Doctor of Philosophy, Graz University of Technology (90000).
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AB - The concurrent recording of the electroencephalogram (EEG) with functional magnetic resonance imaging (fMRI) allows the simultaneous study of the electrophysiology, the blood oxygen level dependent signal, and particularly also their interplay. However, the EEG is affected by a large number of fMRI-related, partly repetitive, artifacts. Average artifact subtraction (AAS) – the most frequently used artifact reduction technique – computes artifact templates from artifact repetitions and subtracts them from the EEG. This effectively reduces repetitive, invariant artifacts, but serious artifact residuals remain. Therefore, this thesis pursued two objectives: analysis of the artifact residuals and development of a new technique for the reduction of the residuals.The inherent variability of artifacts is known to cause residuals after the AAS method, because the subtraction template does not fit the actual artifact. In this thesis, an additional cause of artifact residuals was identified. An intrinsic vulnerability of the AAS technique to correlated artifacts leads to artifact contaminated subtraction templates and consequently to artifact residuals in the EEG. The new artifact reduction technique uses recordings of artifact residuals from a reference-layer EEG cap combined with adaptive filtering to remove the residuals from the EEG and is referred to as reference-layer adaptive filtering (RLAF). The RLAF method is highly effective in offline and online application scenarios. It improves the signal-to-noiseratio as well as the classification accuracy of physiological EEG components substantially. The RLAF technique’s ability to reduce all kinds of artifact residuals – including non-stationary and varying components – in combination with its easy handling, makes it a candidate for a future gold standard method.

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