Real-time MR artifacts filtering during continuous EEG/fMRI acquisition

Garreffa, Girolamo; Carnì, Marco; Gualniera, G.; Ricci, G; Bozzao, L.; Carli, Diego De; Morasso, Pietro; Pantano, P.; Colonnese, Claudio; Roma, V.; Maraviglia, B.

doi:10.1016/j.mri.2003.08.019

Cited by 78 publications

(33 citation statements)

References 12 publications

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“…Additionally, as ALE uses data from fMRI and PET studies, it is important to consider that the blood-oxygen-level-dependent (BOLD) signal and the PET signal are indirect signals. Specifically, the PET signal and BOLD response estimate brain activity by detecting changes associated with blood flow (Logothetis, 2003). Moreover, these indirect signals are typically corrected for motion, smoothed, and averaged across participants.…”

Section: Limitations and Advantages Of Alementioning

confidence: 99%

Common and distinct brain regions in both parietal and frontal cortex support symbolic and nonsymbolic number processing in humans: A functional neuroimaging meta-analysis

et al. 2017

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Common and distinct brain regions in both parietal and frontal cortex support symbolic and nonsymbolic number processing in humans: A functional neuroimaging meta-analysis, NeuroImage, http://dx.doi.org/10. 1016/j.neuroimage.2016.10.028 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. analyses demonstrated anatomically distinct fronto-parietal activation for symbolic and non-symbolic processing. Therefore, symbolic and non-symbolic numbers are subserved by format-dependent and abstract neural systems. Moreover, the present results suggest that regions across the parietal cortex, not just the intraparietal sulcus, are engaged in both symbolic and non-symbolic number processing, challenging the notion that the 2 intraparietal sulcus is the key region for abstract number processing. Additionally, our analyses indicate that the frontal cortex subserve magnitude representations rather than non-numerical cognitive processes associated with number tasks, thereby highlighting the importance of considering both frontal and parietal regions as important for number processing.

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Section: Limitations and Advantages Of Alementioning

confidence: 99%

Common and distinct brain regions in both parietal and frontal cortex support symbolic and nonsymbolic number processing in humans: A functional neuroimaging meta-analysis

et al. 2017

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“…1). In order to detect the peaks, we have used the peak detection algorithms proposed by [28]. For the data under analysis, the value of ST was estimated at 155 ± 1 samples, which corresponds to the time interval of 75.68 ± 0.50 ms [27].…”

Section: ) Peak Detection and St Estimationmentioning

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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“…Localization of those peaks are important for estimation of the echo-planar slice time (ST) as well, parameter used during the gradient artefact correction methodology proposed by Ferreira et al (2013a) and applied in the step 3 of figure 1. In order to detect the peaks, we used the peak detection algorithms proposed by Garreffa et al (2003). Because of the EEG excerpts under analysis were contaminated with transients, making difficult the correct localization of the peaks, we have used the ECG signal recorded simultaneously with the EEG channels to perform the peak detection, as performed by Ferreira et al (2012).…”

Section: Peak Detection and St Estimationmentioning

confidence: 99%

“…Because of the periodic and stationary nature of the gradient artefact waveform, correction methods based upon subtraction in time-domain and its variants have been proposed and successfully employed for artefact correction and subsequent EEG restoration (Allen, et al 2000;Garreffa et al 2003;Gonçalves et al, 2007;De Munck et al, 2013). In this way, the established average artefact subtraction (AAS) methodology proposed by Allen et al (2000) has proven to be very effective for cleaning up imaging artefacts.…”

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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Real-time MR artifacts filtering during continuous EEG/fMRI acquisition

Cited by 78 publications

References 12 publications

Common and distinct brain regions in both parietal and frontal cortex support symbolic and nonsymbolic number processing in humans: A functional neuroimaging meta-analysis

Common and distinct brain regions in both parietal and frontal cortex support symbolic and nonsymbolic number processing in humans: A functional neuroimaging meta-analysis

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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