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| Removing artifacts using Signal Space Projections can sometimes be very efficient. Here are a few tricks to help you remove artifacts that are ongoing (dental work, breathing, noise from a device in the room, etc) or transient. | 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. |
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| Ongoing artifacts can be present in the data with the subject has metal on them or when a device is in the room that is not MEG compatible. Here I will show you two examples. The techniques used here can be applied to most ongoing artifacts. | = General Strategies = We typically use one of three strategies to define an SSP projector: |
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| Metal in the mouth (denal retainer) that moves when the subject breathes. | 1. PCA analysis over one continuous epoch (i.e. the entire recording), for which the artifact is ongoing |
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| 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: Metal in the mouth = 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 two artifacts that are throughout the recording. {{attachment:dental_and_sensor_artifacts.png||height="500",width="600"}} 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. {{attachment:PSD_topo_artifacts.png||height="250",width="600"}} 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. 1. 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 1. Run the process 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. {{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. 1. Now do the same for cardiac events. Select the appropriate component. Here I chose the first one only. |
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: Metal in the mouth
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 two artifacts that are throughout the recording.
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.
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 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.
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. Select the appropriate component. Here I chose the first one only.