Removal of imaging artifacts in EEG during simultaneous EEG/fMRI recording: Reconstruction of a high-precision artifact template

Koskinen, Miika; Vartiainen, Nuutti

doi:10.1016/j.neuroimage.2009.01.061

Cited by 12 publications

(13 citation statements)

References 16 publications

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“…The signals were bandpass filtered at 0.016 -1000 Hz and sampled at 5000 Hz. The induced EPI artifacts in EEG recordings were removed (Koskinen and Vartiainen, 2009) and the data resampled to 200 Hz. In MEG, EEG data were recorded using a 64-channel cap (Elekta Neuromag), bandpass filtered at 0.03-200 Hz, and digitized at 600 Hz.…”

Section: Eeg Data Acquisition and Analysismentioning

confidence: 99%

Functional Magnetic Resonance Imaging Blood Oxygenation Level-Dependent Signal and Magnetoencephalography Evoked Responses Yield Different Neural Functionality in Reading

Vartiainen¹,

Liljeström²,

Koskinen³

et al. 2011

J. Neurosci.

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It is often implicitly assumed that the neural activation patterns revealed by hemodynamic methods, such as functional magnetic resonance imaging (fMRI), and electrophysiological methods, such as magnetoencephalography (MEG) and electroencephalography (EEG), are comparable. In early sensory processing that seems to be the case, but the assumption may not be correct in high-level cognitive tasks. For example, MEG and fMRI literature of single-word reading suggests differences in cortical activation, but direct comparisons are lacking. Here, while the same human participants performed the same reading task, analysis of MEG evoked responses and fMRI blood oxygenation level-dependent (BOLD) signals revealed marked functional and spatial differences in several cortical areas outside the visual cortex. Divergent patterns of activation were observed in the frontal and temporal cortex, in accordance with previous separate MEG and fMRI studies of reading. Furthermore, opposite stimulus effects in the MEG and fMRI measures were detected in the left occipitotemporal cortex: MEG evoked responses were stronger to letter than symbol strings, whereas the fMRI BOLD signal was stronger to symbol than letter strings. The EEG recorded simultaneously during MEG and fMRI did not indicate neurophysiological differences that could explain the observed functional discrepancies between the MEG and fMRI results. Acknowledgment of the complementary nature of hemodynamic and electrophysiological measures, as reported here in a cognitive task using evoked response analysis in MEG and BOLD signal analysis in fMRI, represents an essential step toward an informed use of multimodal imaging that reaches beyond mere combination of location and timing of neural activation.

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Section: Eeg Data Acquisition and Analysismentioning

confidence: 99%

Functional Magnetic Resonance Imaging Blood Oxygenation Level-Dependent Signal and Magnetoencephalography Evoked Responses Yield Different Neural Functionality in Reading

Vartiainen¹,

Liljeström²,

Koskinen³

et al. 2011

J. Neurosci.

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“…Such characteristics shall be better evaluated in future work. Since estimation of the artefact interference is not based upon the subtraction of an artefact waveform model or template [4,11,13,17], resulting subtraction residuals are negligible within application of our method, which enables its use for correction of EEG signals with small amplitude. Another advantage of using the proposed approach is that there is no need for time-alignment between EEG and fMRI clocks as well as extensive data segmentation.…”

Section: Discussionmentioning

confidence: 99%

“…However, the performance of the AAS method depends on accurate sampling of the gradient artefact waveform. It requires time-alignment between fMRI scanner and EEG clocks as well [10,11,12,13]. Head motions of the subject within the fMRI scanner can also compromise its efficacy, because of the alterations and transients that are inserted in the artefact waveform, resulting in an inaccurate averaging process [14].…”

Section: Introductionmentioning

confidence: 99%

Optimized moving-average filtering for gradient artefact correction during simultaneous EEG-fMRI

Ferreira

Aarts

Cluitmans

2014

5th ISSNIP-IEEE Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC)

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Abstract-The strong capability of the combined EEG-fMRI for investigating and revealing new insights on mapping of the brain activity as well as on several other neuroscientific studies has attracted the interest of researchers and clinicians over the past years. However, its consolidation as a powerful and independent technique still depends on enhancing the quality of the EEG signal, mainly due to the occurrence of artefacts. This paper presents a simple and effective approach for removal of the gradient artefact, which is induced in the EEG by the rapidly varying gradient magnetic fields of the fMRI scanner. According to our method, a moving-average filter is used to perform the removal of the gradient artefact. Nevertheless, rather than estimation of an artefact waveform template to be subtracted and achieve the EEG restoration, we have proposed to optimize the moving-average filtering process along the entire EEG excerpt. Thereby, the restored EEG can be estimated either from a sum of partial waveform components resulting from the recursive application of the optimized moving-average filter; or from an estimative of the artefact along the entire excerpt. Our methodology shows to achieve a quite satisfactory restoration of the EEG signal, even for low signal amplitudes. Moreover, in addition to predict the variability of the artefact waveform over the time, synchronization between EEG and fMRI clocks and extensive data segmentation are not required as well.

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“…Moreover, the data can be modelled by respecting constraints of smoothness and monotonicity. The usage of cubic splines was proposed by Koskinen and Vartiainen (2009) to improve the artefact template estimation during application of the AAS method (Allen et al, 2000).…”

Section: Signal Transients Modelling and Subtractionmentioning

confidence: 99%

“…However, as discussed by Yan et al (2009), head motions of the subject within the fMRI scanner compromise its efficacy because of the alterations and transients that are inserted in the artefact waveform. Thereby, the averaging process results in an inaccurate estimation of the artefact template (Sun and Hinrichs, 2009;Koskinen and Vartiainen, 2009), which leads to arising residual artefacts in the corrected EEG.…”

Section: Introductionmentioning

confidence: 99%

Removal of Gradient Artefacts during Transient Head Movements for Continuous EEG-fMRI

Ferreira

Aarts

Cluitmans

2014

Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing

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Abstract:This paper presents a novel approach for removing gradient artefacts from the EEG signal recorded during continuous EEG-fMRI, which are influenced by transient head movements of the subject within the magnetic scanner. Transient head movements provoke abrupt changes in the gradient artefact waveform, in such a way that they compromise the estimation of an artefact waveform to be subtracted and achieve the EEG correction. According to our proposed methodology, a cubic spline waveform is used to model and represent the signal transitions components. This model is then used to change and approximate the shape of the EEG signal as homogeneous data, in order to improve the performance of the gradient artefact correction technique. The proposed approach also makes use of the signal slope adaption (SSD) method, combined with sum-of-sinusoids modelling for correction of the gradient artefact. Our methodology reveals to perform a robust and satisfactory removal of gradient artefacts under the occurrence of abrupt transient head movements.

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Removal of imaging artifacts in EEG during simultaneous EEG/fMRI recording: Reconstruction of a high-precision artifact template

Cited by 12 publications

References 16 publications

Functional Magnetic Resonance Imaging Blood Oxygenation Level-Dependent Signal and Magnetoencephalography Evoked Responses Yield Different Neural Functionality in Reading

Functional Magnetic Resonance Imaging Blood Oxygenation Level-Dependent Signal and Magnetoencephalography Evoked Responses Yield Different Neural Functionality in Reading

Optimized moving-average filtering for gradient artefact correction during simultaneous EEG-fMRI

Removal of Gradient Artefacts during Transient Head Movements for Continuous EEG-fMRI

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