Tutorial 12: Artifact detection

Authors: Francois Tadel, Elizabeth Bock, Sylvain Baillet

The previous tutorial illustrated how to remove noise patterns occurring continuously and at specific frequencies. However, most of the events that contaminate the MEG/EEG recordings are not permanent, span over a large frequency range or overlap with the frequencies of the brain signals of interest. Frequency filters are not appropriate to correct for eye movements, breathing movements, heartbeats or other muscle activity.

For getting rid of reproducible artifacts, one popular approach is the Signal-Space Projection (SSP). This method is based on the spatial decomposition of the MEG/EEG recordings for a selection of time samples during which the artifact is present. Therefore we need to identify when each type of artifact is occurring in the recordings. This tutorial shows how to detect automatically some well defined artifacts: the blinks and the heartbeats.

Observation

Let's start by observing the type of contamination the blinks and heartbeats cause to the MEG recordings.

Detection: Heartbeats

In the Record tab, select the menu: "Artifacts > Detect heartbeats".

Now do the same thing for the blinks: Menu "Artifacts > Detect eye blinks".

Remove simultaneous blinks/heartbeats

We will use these event markers as the input to our SSP cleaning method. This technique works well if each artifact is defined precisely and as independently as possible from the other artifacts. This means that we should try to avoid having two different artifacts marked at the same time.

Because the heart beats every second or so, there is a high chance that when the subject blinks there is a heartbeat not too far away in the recordings. We cannot remove all the blinks that are contaminated with a heartbeat because we would have no data left. But we have a lot of heartbeats, so can do the contrary: remove the markers "cardiac" that are occurring during a blink.

In the Record tab, select the menu "Artifacts > Remove simultaneous". Set the options:

After executing this process, the number of "cardiac" events goes from 464 to 455. The deleted heartbeats were all less than 250ms away from a blink.

Run #02: Running from a script

Let's perform the same detection operations on Run #02, using this time the Process1 box.

Advanced

Custom detection

These two processes "Detect heartbeats" and "Detect eye blinks" are in reality shortcuts for a generic process "Detect custom events". This process can be used for detecting any kind of event based on the signal power in a specific frequency band. We are not going to use it here, but you may have to use it if the standard parameters do not work well, or for detecting other types of events.

Advanced

In case of failure

If the signals are not as clean as in this sample dataset, the automatic detection of the heartbeats and blinks may fail with the standard parameters. You may have to use the process "Detect custom events" and adjust some parameters. For instance:

If you cannot get your artifacts to be detected automatically, you can browse through the recordings and mark all the artifacts manually, as explained in the tutorial Event markers.

Additional documentation








Feedback: Comments, bug reports, suggestions, questions
Email address (if you expect an answer):


Tutorials/ArtifactsDetect (last edited 2015-07-10 21:12:11 by FrancoisTadel)