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| = Example 1: Metal in the mouth = | = Example 1: removing artifacts that are continuous = |
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| The first step is to identify the artifact and channel(s) that are most affected. After scanning though the data, it appears there are two artifacts that are throughout the recording. | The first step is to identify the artifact and channel(s) that are most affected. After scanning though the data, it appears there are some artifacts present throughout the recording. |
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| First, slower perturbations in the posterior temporal areas, as seen in the channels highlighted in red. Then there are and sharp 'jumps', highlighted in blue, which do not seem to be physiological. In addition, performing a power spectrum density (PSD) helps identify the channels and the frequencies which are most affected by these artifacts. | First, slower perturbations in the posterior temporal areas, as seen in the channels highlighted in red. Then there are sharp 'jumps', highlighted in blue, which do not seem to be physiological, but instead are most likely sensor noise. We can also see artifacts from the heart. In addition, performing a power spectrum density (PSD) helps identify the channels and the frequencies which are most affected by these artifacts. |
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| 1. Review the resulting projectors and find component(s) that eliminates the artifact. Be sure to review the topography and the time series of the component to be sure you are only removing the artifact. | 1. Review the resulting projectors and find component(s) that eliminate the artifact. Be sure to review the topography and the time series of the component to confirm you are only removing the artifact. |
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| {{attachment:eight_components_PCA.png||height="400",width="600"}} We can see here that the first two components are most likely artifacts. Selecting these two does a very good job of removing the artifact. | {{attachment:eight_components_PCA.png||height="400",width="600"}} |
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| 1. Now do the same for cardiac events. Select the appropriate component. Here I chose the first one only. | We can see here that the first two components are most likely artifacts. Selecting these two does a very good job of removing the artifact. {{attachment:dental_and_sensor_artifacts_cleaned.png||height="500",width="600"}} 1. Now do the same for cardiac events. Detect the events either from the ECG channel or from an MEG sensor. In this case, we do not have an extra ECG channel and therefore MRT41 is used. Compute the SSP on those cardiac events. Select the appropriate component. I chose the first one component because it represented the artifact. {{attachment:cardiac_artifact_before.png||height="250",width="300"}} {{attachment:cardiac_artifact_after.png||height="250",width="300"}} Before (left) and after (right) cardiac SSP correction. = Example 2: define an SSP from the topography of an artifact = Here is an example with an artifact from a 3D video monitor that was placed inside the MSR Reviewing the MEG channels and power spectrum reveals a strong artifact in the frontal sensors at 24Hz and its harmonics. We also see eye blink artifact. {{attachment:artifacts_before_SSPs.png||height="500",width="600"}} If we zoom in a bit to see the artifact clearer, we can see that there is a strong repeating topography. We will make a projector with this topography. {{attachment:grab_SSP_from_topo.png||height="400",width="250"}} 1. Right-click on the topography -> Snapshot -> Save as SSP projector, then save to disk. 1. From the Record tab, in the Artifacts menu-> Select active projector, then click Load projectors and select the file that was just saved. This will import and apply the projector. Now just the eyeblinks remain. Detect the eye blink events (with either the EOG channel or one of the MEG channels showing the artifact). Compute the SSP and select the appropriate projector. The resulting projectors and the clean data are shown here: {{attachment:SSP_topos_for_artifacts.png||height="500",width="600"}} {{attachment:artifacts_after_SSPs.png||height="500",width="600"}} |
Removing artifacts from MEG recordings using Signal Space Projections can sometimes be very efficient. Here are a few tricks to help you remove artifacts from your data that are ongoing or repeating.
General Strategies
We typically use one of three strategies to define an SSP projector:
1. PCA analysis over one continuous epoch (i.e. the entire recording), for which the artifact is ongoing
2. PCA analysis for concatenated 'events', for which the artifact is discrete and repeating in the recording
3. Visual identification of one topography that represents the artifact and is converted to an SSP projector.
Example 1: removing artifacts that are continuous
Here the subject has metal dental work that moves when he breathes.
The first step is to identify the artifact and channel(s) that are most affected. After scanning though the data, it appears there are some artifacts present throughout the recording.
First, slower perturbations in the posterior temporal areas, as seen in the channels highlighted in red. Then there are sharp 'jumps', highlighted in blue, which do not seem to be physiological, but instead are most likely sensor noise. We can also see artifacts from the heart.
In addition, performing a power spectrum density (PSD) helps identify the channels and the frequencies which are most affected by these artifacts.
The temporal channels have higher power in the lower freq ([0,6]Hz) and the topography shows a dipole below the posterior part of the sensor array.
Since the slower artifact is ongoing and not necessarily a well-defined 'event', we will use the first strategy.
- Using the Artifacts menu, select SSP: Generic
- Time window: this should be the time window of the artifact. Here I use the first 100 seconds of the recording since the event is well defined in this time, but you can use longer times to better define the event if necessary.
- Event name: empty
- Event window: ignore this
- Frequency band: from the PSD I can see that the artifact is mostly defined in [0.5,10]Hz. If you know the freq band of the artifact, you should adjust this accordingly, if not, you can use a more broadband to get an overall view.
- Sensor types: MEG
- Compute using existing: uncheck this box
- Save averaged artifact: uncheck this box
- Method to calculate: choose PCA
- Run the process
- Review the resulting projectors and find component(s) that eliminate the artifact. Be sure to review the topography and the time series of the component to confirm you are only removing the artifact.
We can see here that the first two components are most likely artifacts. Selecting these two does a very good job of removing the artifact.
- Now do the same for cardiac events. Detect the events either from the ECG channel or from an MEG sensor. In this case, we do not have an extra ECG channel and therefore MRT41 is used. Compute the SSP on those cardiac events. Select the appropriate component. I chose the first one component because it represented the artifact.
Before (left) and after (right) cardiac SSP correction.
Example 2: define an SSP from the topography of an artifact
Here is an example with an artifact from a 3D video monitor that was placed inside the MSR
Reviewing the MEG channels and power spectrum reveals a strong artifact in the frontal sensors at 24Hz and its harmonics. We also see eye blink artifact.
If we zoom in a bit to see the artifact clearer, we can see that there is a strong repeating topography. We will make a projector with this topography. 
Right-click on the topography -> Snapshot -> Save as SSP projector, then save to disk.
From the Record tab, in the Artifacts menu-> Select active projector, then click Load projectors and select the file that was just saved. This will import and apply the projector.
Now just the eyeblinks remain. Detect the eye blink events (with either the EOG channel or one of the MEG channels showing the artifact). Compute the SSP and select the appropriate projector.
The resulting projectors and the clean data are shown here: 
