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

Abstract

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.

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

“…Finally, figure 8 reveals that the usage of the cubic spline for representation of the signal transitions can be employed to improve the gradient artefact correction obtained by the AAS method (Allen et al, 2000, Moosmann et al, 2009). For evaluation of this case scenario, we used this approach in the step 3 of figure 1 as well, after application of the non-linear filter by SSD (Ferreira et al, 2013b;Ferreira et al, 2013c) in the signals of figure 4. Taking into account the signal of figure 4b, it can be noticed that the EEG restoration obtained by both correction methods are quite similar (figure 8).…”

“…According to this method, initially a non-linear filter based upon the signal slope adaption (SSD) approach (Ferreira et al, 2013b;Ferreira et al, 2013c) is applied to the raw EEG in order to remove artefact high-frequency components. Here, we have applied this filter directly to the signal S h , as suggested earlier.…”

Gradient Artefact Correction and Evaluation of the EEG Recorded Simultaneously with fMRI Data Using Optimised Moving-Average

“…This integral can be described as a moving-average filter with order M : Because of the phase distortion provoked by the moving-average filter [ 44 – 46 ], the mean value is not in phase with the neuronal EEG, e true, n − k . In order to make them in phase, the moving-average must be backward applied in ( 3 ): Thereby, according to ( 4 ) and ( 5 ), forward-backward application of the moving-average filter in the recorded scalp potential results in the signal e comp,1 that is in phase and constitutes a mean approximation of the neuronal EEG [ 38 ]: Equation ( 5 ) acts as a smoothing filter, in such a way that the signal e comp,1 contains low-frequency activity associated with . In turn, the frequency activity associated with the gradient artefact is contained in the signal, e high,1 , resulting from the subtraction of e comp,1 from s : Since high-frequency components associated with remain in e high,1 , it is possible to obtain an estimate of such components by the iterative application of ( 5 ) in e high,1 .…”

“…This paper presents a novel methodology for gradient artefact correction based upon optimised moving-averaging (OMA) filtering [ 38 ]. OMA filtering constitutes a modality of iterative filtering decomposition [ 39 , 40 ] and has been exploited in a research project that our group has undertaken to investigate characteristics and features of the gradient artefact that might be used to attenuate, correct, and improve the quality of the corrected EEG signal [ 38 , 41 – 43 ]. Optimised moving-average makes use of forward-backward application of a moving-average (MA) filter as an integration procedure to suppress the artefact and estimate partial components of the corrected EEG at the same time.…”

Application of mathematical morphology for power quality improvement in microgrid

“…SHAF is one of the possible solutions for mitigating the harmonic problem . In previous works, the detailed working principle of SHAF is explained by using different generations and loads. However, the control approach of SHAF uses conventional αβ / dq transformation, which makes the circuit more complex and creates a large computational error.…”