BESA Research Artifact Correction - Revision history

Robert: /* References */

2021-05-04T13:03:53Z

‎References

Robert: /* Using Defined Topographies for Correction */

2021-05-04T13:00:14Z

‎Using Defined Topographies for Correction

Robert: /* Introduction */

2021-05-04T12:57:45Z

‎Introduction

Robert: /* Automatic Artifact Correction */

2020-11-09T14:09:54Z

‎Automatic Artifact Correction

Jaehyun: /* fMRI artifact removal */

2019-04-02T15:08:07Z

‎fMRI artifact removal

Jamie: /* fMRI artifact removal */

2019-03-27T08:36:17Z

‎fMRI artifact removal

Jamie at 13:13, 26 March 2019

2019-03-26T13:13:31Z

Jamie: /* Good Practice */

2019-03-26T13:08:43Z

‎Good Practice

Jamie: /* Filtration Methods */

2019-03-26T13:04:19Z

‎Filtration Methods

Jamie: /* Filtration Methods */

2019-03-26T13:03:32Z

‎Filtration Methods

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 *Allen, P.J., Josephs, O, Turner, R, 2000 A Method for Removing Imaging Artifact from Continuous EEG Recorded during Functional MRI. NeuroImage 12, 230–239 https://doi.org/10.1006/nimg.2000.0599
 *Moosmann, M, Schönfelder, V.H., Specht, K, Scheeringa, R, Nordby, H, Hugdahl, K, 2009. Realignment parameter-informed artefact correction for simultaneous EEG–fMRI recordings. NeuroImage 45, 1144–1150 https://doi.org/10.1016/j.neuroimage.2009.01.024
-*Mullinger, K.J., Yan, W.X., Bowtell, R, 2011. Reducing the gradient artefact in simultaneous EEG-fMRI by adjusting the subject’s axial position. NeuroImage 54, 1942–1950.
+*Mullinger, K.J., Yan, W.X., Bowtell, R, 2011. Reducing the gradient artefact in simultaneous EEG-fMRI by adjusting the subject’s axial position. NeuroImage 54, 1942–1950, https://doi.org/10.1016/j.neuroimage.2010.09.079
 *Rusiniak, M, Lewandowska, M, Wolak, T, Pluta, A, Milner, R, Ganc, M, Włodarczyk, A, Senderski, A, Śliwa, L, Skarżyński, H, 2013a. A modified oddball paradigm for investigation of neural correlates of attention a simultaneous ERP–fMRI study Magn Reson Mater Phys Biol Med 26, 511–526 https://doi.org/10.1007/s10334-013-0374-7
 *Rusiniak, M, Wolak, T, Lewandowska, M, Cieśla, K, Skarzynski, H, 2013b. The relation between EPI sequence parameters and electroencephalographic data during simultaneus EEG-fMRI registration an initial report, in ESMRMB 2013 Congress, Book of Abstracts, Saturday Presented at the ESMRMB, Springer, Toulouse, p 661 https://doi.org/10.1007/s10334-013-0384-5

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-This starts an automatic average of all matching patterns in the data file. You may stop the search after a number of events were found (progress is shown in the status bar; stop averaging with the '<span style="color:#3366ff;">'''ESC'''</span>key)'. When asked for a filename to save the pattern, press <span style="color:#3366ff;">'''Cancel'''</span>. The averaged data buffer containing the artifact is shown. Now use the averaged artifact to define the artifact topography as described above for the blink artifact. When selecting the artifact category, choose EKG:
+This starts an automatic average of all matching patterns in the data file. You may stop the search after a number of events were found (progress is shown in the status bar; stop averaging with the <span style="color:#3366ff;">'''ESC'''</span> key). When asked for a filename to save the pattern, press <span style="color:#3366ff;">'''Cancel'''</span>. The averaged data buffer containing the artifact is shown. Now use the averaged artifact to define the artifact topography as described above for the blink artifact. When selecting the artifact category, choose EKG:
 [[Image:Artifact Correction (11).gif]]
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 More information on artifact correction is given in the help chapter "''Artifact Correction / Tutorial''".
 ==fMRI artifact removal==

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 ** '''Develop dipole models in the presence of spatial components'''. This is the preferred method. Create separate averages of the ''uncorrected'' conditions and of the artifact events, e.g. blinks. All averages can be concatenated into one binary segment file (<span style="color:#ff9c00;">'''<nowiki>*.fsg</nowiki>'''</span>). The averaged artifacts should then be used to define spatial components during source analysis. Thus, the time course of the artifacts and of the event-related source activity can be separated and observed in the source analysis window.
 ** '''Develop dipole models on the pre-corrected averages'''. Using subspace-projection (SSP), the artifact dimensions are compensated for in source models. This will lead to the same dipole models as above, but a) the time course of artifact activity is not available, and b) residual variance is lower because variance from dimensions in data space occupied by the artifact spatial components are removed from the data by SSP. It does not matter which correction method was used with BESA Research (PCA, Surrogate, or SSP) – the results of source analysis are the same.
-* No artifact correction. If the data contain only a few artifacts, use artifact rejection rather than correction. This avoids the distortion resulting from correction.
+* '''No artifact correction'''. If the data contain only a few artifacts, use artifact rejection rather than correction. This avoids the distortion resulting from correction.

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 == Automatic Artifact Correction ==
-Automatic correction is the easiest way to obtain artifact-cleared data for review. The estimation of the correction coefficients can be done using the whole EEG or an epoch defined by markers. If only one marker is set, the EEG is scanned from the beginning to the marker, or from the marker to the end, depending on the current position of the display. The longer the range, the more precisely the topography of the artifact can be defined. However, longer ranges increase computation time.
+BESA Research provides an automatic artifact correction method that enables users to quickly obtain artifact-cleared data. For more details see the main article on [http://wiki.besa.de/index.php?title=BESA_Research_Automatic_Artifact_Correction Automatic Artifact Correction]
-−
-In the Review Window, select the menu entry <span style="color:#3366ff;">'''Artifact / Automatic'''</span> to bring up the artifact selection dialog box:
-−
-[[Image:Artifact Correction (3).gif]]
-−
-−
-By default, EOG correction is computed using an internal model of eye artifact topographies. For automatic EKG correction, an EKG channel has to be defined. If markers are defined (see the figure below), the option ''Between Markers'' is ticked automatically.
-−
-Clicking <span style="color:#3366ff;">'''OK'''</span> starts the assignment of the artifact topographies, which can be interrupted at any time using the <span style="color:#3366ff;">''''ESC' '''</span>key on the keyboard.
-−
-−
-<!-- [[Image:Artifact Correction (4).gif|400px]] -->
-[[Image:Artifact Correction (4) new.png|500px]]
-−
-''A block of EEG (the example file ''<span style="color:#ff9c00;">'''Examples\EEG-FOCUS\eeg2.eeg'''</span>'') is shown containing eye artifacts. Markers have been placed on either side of the artifacts.''
-−
-−
-<!-- [[Image:Artifact Correction (5).gif|400px]] -->
-[[Image:Artifact Correction (5) new.png|500px]]
-−
-''Waveform display after automatic artifact correction. Virtual channels which carry the artifact signal are automatically appended at the bottom.''
-−
-−
-The corrected waveforms are shown, together with the channels HEOG and VEOG that depict the artifact signals. The oscillatory brain activity is not distorted by artifact correction. Note that correction is applied to scalp channels only. Non-scalp channels, e.g. polygraphic channels, are not corrected and are therefore unaffected by the correction.
-−
-−
-[[Image:Artifact Correction (6).gif]]
-−
-''Above, a later segment of the EEG is shown, using the TA_Temporal-Lobe source montage, with and without correction. Note that, without artifact correction, the eye movements are depicted on the channels representing anterior temporal lobe and frontal brain activity. After correction, the artifacts are greatly reduced without distorting the topography of the periodic discharges.''
 == Using Defined Topographies for Correction ==

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 ===Introduction===
 <u>Currently data collected during simultaneous EEG-fMRI is not intended to be used in any diagnostic procedures. Following these guidelines, fMRI artifact removal implemented in BESA Research may also not be used for any diagnostic procedures This tool was developed only for research purposes.
 <u>Currently data collected during simultaneous EEG-fMRI is not intended to be used in any diagnostic procedures. Following these guidelines, fMRI artifact removal implemented in
 BESA Research may also not be used for any diagnostic procedures This tool was developed only for research purposes.
-−
 Please use only EEG hardware that is certified for MRI!</u>
-When EEG data is recorded during an fMRI session there are plenty problems to solve to be able to analyze the real brain signal The first one is related to fast switching of MRI
+When EEG data is recorded during an fMRI session there are plenty problems to solve to be able to analyze the real brain signal The first one is related to fast switching of MRI gradient coils which is a part of volume acquisition in the Echo-Planar sequence used for fMRI imaging This switching introduces variable magnetic fields that induce variable electrical fields in the EEG recording system (electrodes and wires namely), as described by Maxwell’s second law The induced artifact is typically very large in amplitude (typically 16 mV in a 3.0 T MR scanner). Also, the frequency range of the artifact starts in the frequency band of the EEG signal The artifact frequency and its harmonics can be easily assessed using the following equation (Rusiniak et al, 2013b):
 gradient coils which is a part of volume acquisition in the Echo-Planar sequence used for fMRI imaging This switching introduces variable magnetic fields that induce variable
 electrical fields in the EEG recording system (electrodes and wires namely), as described by Maxwell’s second law The induced artifact is typically very large in amplitude (typically
-mV in a 3T MR scanner) Also, the frequency range of the artifact starts in the frequency band of the EEG signal The artifact frequency and its harmonics can be easily assessed
 using the following equation (Rusiniak et al, 2013b):
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-where f<sub>x</sub> is the harmonic frequency, x is the harmonic number, NS represents the number of slices, and TR stands for Repetition Time of fMRI volume acquisition.
+where f<sub>x</sub> is the harmonic frequency, x is the harmonic number, NS represents the number of slices, and TR stands for Repetition Time of fMRI volume acquisition. From a variety of methods, the most relevant successful and best known are those based on moving average subtraction as firstly introduces by Allen and colleagues (2000). These methods work only when the following assumptions are true:
 From a variety of methods, the most relevant successful and best known are those based on moving average subtraction as firstly introduces by Allen and colleagues(2000). These
 methods work only when the following assumptions are true:
 #Each artifact occurrence is identical: Each fMRI volume acquisition is identical (gradient switchingdoes not alter between volumes) so in general all artifacts are identical However, if the subject moves during the acquisition the artifact changes because electrode positions in relation to the local magnetic field have changed.
 #EEG signal is stationary: In principle this assumption is true for the EEG signal. The signal stationaritycan be affected by the paradigm, therefore jitter between triggers is strongly advised.
-#Each start points of artifacts have to be precisely indicated: This can be achieved either by hardwareclock synchronization between EEG system and MRI machine or by a very high sampling rate (>10kHz). All modern EEG hardware units designed for simultaneous EEG-fMRI registration are capable of clock synchronization.
+#Each start points of artifacts have to be precisely indicated: This can be achieved either by hardwareclock synchronization between EEG system and MRI machine or by a very high sampling rate (>10 kHz). All modern EEG hardware units designed for simultaneous EEG-fMRI registration are capable of clock synchronization.
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+[[Image:FMRI_articact_removal1.png|900px]]
 [[Image:FMRI_articact_removal1.png]]
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 * '''Removal method''' In BESA, three methods are available (see below for more details) The last two methods are available only if a correctly configured '''realignment file''' is provided.
 * '''Template creation matrix''' Visualization matrix that illustrates how the filtration is performed Check Filtration Methods description below for further details.
 ===Filtration Methods===
-* '''Allen et al, 2000'''
+==== Allen et al, 2000 ====
 This method is a starting point for the two other methods as well In all methods a moving average artifact subtraction is used and the difference between methods can be noted in the template creation matrix
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-, where ''m'' is the number of fMRI volumes recorded, and ''k'' is the number of artefact volumes used for template creation (averaging).
+, where ''m'' is the number of fMRI volumes recorded, and ''k'' is the number of artifact volumes used for template creation (averaging).
 After the artifact template for every epoch is created the final subtraction can be performed:
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-To clean the epoch indicated by the green shaded box the surrounding epochs have to be averaged first (here the averaging constant is k=10) After the template is created, it issubtracted from the recorded data at each channel independently to obtain artefact free data Next, this procedure is repeated for every epoch related to fMRI acquisition as indicated in the template creation matrix:
+To clean the epoch indicated by the green shaded box the surrounding epochs have to be averaged first (here the averaging constant is k=10) After the template is created, it is subtracted from the recorded data at each channel independently to obtain artifact free data Next, this procedure is repeated for every epoch related to fMRI acquisition as indicated in the template creation matrix:
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-* Allen et al, 2000 Modified
+==== Allen et al, 2000 Modified ====
-This method is available only if a correctly configured realignment file is provided Also, the changes in filtration will be visible if there is significant movement in the data (a recommended value for the movement threshold is 0.3 mm) In the modified Allen method, the epochs where subject’s movement is exceeding the threshold are ignored during artefact template creation, as shown in the template creation matrix below Note the vertical white lines – they represent the moments were movement was higher than threshold The data will be filtered but less artifact occurrences will be used for template creation close to a movement area.
+This method is available only if a correctly configured realignment file is provided Also, the changes in filtration will be visible if there is significant movement in the data (a recommended value for the movement threshold is 0.3 mm) In the modified Allen method, the epochs where subject’s movement is exceeding the threshold are ignored during artifact template creation, as shown in the template creation matrix below Note the vertical white lines – they represent the moments were movement was higher than threshold The data will be filtered but less artifact occurrences will be used for template creation close to a movement area.
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-* '''Moosmann et al, 2009'''
+==== Moosmann et al, 2009 ====
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 Please keep in mind that simultaneous EEG-fMRI recording is a difficult yet powerful technique Following these rules could help one to perform a successful experiment:
-*For an ERP experiment remember to design it with jitter between trials (i.e. random value of ±200ms) Also, applying a pure EEG or pure fMRI paradigm will probably have the effect that one of the modalities will not show satisfying results Proper paradigm preparation is essential for success Some further guidelines can be found here:(Rusiniak et al, 2013a)
+*For an ERP experiment remember to design it with jitter between trials (i.e. random value of ±200 ms) Also, applying a pure EEG or pure fMRI paradigm will probably have the effect that one of the modalities will not show satisfying results Proper paradigm preparation is essential for success Some further guidelines can be found here:(Rusiniak et al, 2013a)
 *Inform your subject how important it is not to move during the acquisition.
 *Keep electrode to skin impedance as low as possible.
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 ===Balistocardiogram and other artifacts===
-After removing the fMRI gradient artefact many different steps can be performed (Abreu et al, 2018) For ECG induced artifacts (balistocardiogram) we recommend artifact correction
-using defined topographies for correction (usually with more than 3 components to filter).
+After removing the fMRI gradient artifact many different steps can be performed (Abreu et al, 2018) For ECG induced artifacts (balistocardiogram) we recommend artifact correction using defined topographies for correction (usually with more than 3 components to filter).
 ===References===

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 *Especially for the first few registrations repeat the experiment outside of the bore to compare results
 *Remember that conditions in MRI are far from standard EEG recording The long supine position, electrode pressure on back of the head, huge noise and stress can affect the EEG!
 == Artifacts Tutorial: Using Calibration Data to Generate Artifact Coefficients ==

← Older revision		Revision as of 13:08, 26 March 2019
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	===Good Practice===		===Good Practice===

		+	Please keep in mind that simultaneous EEG-fMRI recording is a difficult yet powerful technique Following these rules could help one to perform a successful experiment:

		+	*For an ERP experiment remember to design it with jitter between trials (i.e. random value of ±200ms) Also, applying a pure EEG or pure fMRI paradigm will probably have the effect that one of the modalities will not show satisfying results Proper paradigm preparation is essential for success Some further guidelines can be found here:(Rusiniak et al, 2013a)
		+	*Inform your subject how important it is not to move during the acquisition.
		+	*Keep electrode to skin impedance as low as possible.
		+	*The EEG-fMRI recording session should be long enough to allow for proper artifact creation Usually an experiment should last at least 6 minutes.
		+	*At the same time try to limit time of experiment to a minimum and preferably perform EEG-fMRI registration before other sequences to limit movement due to inconvenient supine position.
		+	*Place subject about 4 cm further in caudal direction to reduce artifacts The MRI laser crosshair should point not to the Nasion position but to the middle of the forehead (Mullinger et al, 2011)
		+	*Especially for the first few registrations repeat the experiment outside of the bore to compare results
		+	*Remember that conditions in MRI are far from standard EEG recording The long supine position, electrode pressure on back of the head, huge noise and stress can affect the EEG!

	== Artifacts Tutorial: Using Calibration Data to Generate Artifact Coefficients ==		== Artifacts Tutorial: Using Calibration Data to Generate Artifact Coefficients ==

← Older revision		Revision as of 13:04, 26 March 2019
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−	, where ''m'' is the number of fMRI volumes recorded, and k is the number of artefact volumes used for template creation (averaging).	+	, where ''m'' is the number of fMRI volumes recorded, and ''k'' is the number of artefact volumes used for template creation (averaging).
	After the artifact template for every epoch is created the final subtraction can be performed:		After the artifact template for every epoch is created the final subtraction can be performed:

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-, where m is the number of fMRI volumes recorded, and k is the number of artefact volumes used for template creation (averaging).
+, where ''m'' is the number of fMRI volumes recorded, and k is the number of artefact volumes used for template creation (averaging).
 After the artifact template for every epoch is created the final subtraction can be performed:
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 Note that this operation results in a different shape of matrix Importantly there are also white vertical lines This indicates that there was more than one epoch contaminated with movement In such situations, the matrix is transformed to adjust to fit to the largest movements, and smaller movements are treated as in Allen et al, 2000 Modified method.
 ===Good Practice===