Tutorial 14: Additional bad segments

Authors: Francois Tadel, Elizabeth Bock, Sylvain Baillet

We have already corrected the recordings for the artifacts at fixed frequencies (power lines) and for some standard and reproducible artifacts (heartbeats and blinks). There are many other possible sources of noise that can make the recordings unusable in our analysis. This tutorial introduces how to identify and mark these bad segments.

Manual inspection

It is very important to mark bad segments (noisy sections) of the recordings before running any fancy analysis. It may save you hours of repeated work later, when you discover after processing all your data that you have to redo everything because you have left some huge artifacts in the recordings.

In terms of quality control, there is no automatic method that will give you results as good as a manual screening of the recordings. We recommend you always take a few minutes to scroll through all your files to identify and tag all the noisy segments. Do this full screening after you're done with all the other pre-processing steps (filtering and SSP/ICA cleaning) to remove what has not been corrected with other techniques.

At the beginning, it is not easy to separate what is too noisy from what is acceptable. This is usually an iterative process: at the first attempt you guess, you average the trials and estimate the sources and finally realize that are some eye movements left that are masking your effect of interest. You have to delete everything, add some bad segments and try again. On the contrary, if you reject too much data at the beginning, you may not have enough trials to observe your effect correctly. The balance is not easy to find, but you'll get good at it quickly. Brainstorm offers tools to do these operations easily, so a few trials and errors are not too dramatic. Just make sure you check the quality of your data at every step of the analysis, so that you don't go too far in the wrong direction.

To review your recordings and check for major artifacts, you can for instance:

When you identify something that doesn't look good:

Automatic detection

We have developed some tools to help with this screening procedure. The process "Artifacts > Detect other artifacts" identifies epochs of time that contain typical artifacts from eye movement, subject movement or muscle contractions. While it is still advised that you visually inspect all of your data, this process can help identify areas that contain artifacts which you may want to mark as bad segments.

Currently, the process runs only on continuous raw links and identifies artifacts in two frequency bands, chosen because of the predictability of artifacts in these bands.

Important notes

Recommendations for usage

Run #01

We will now apply this process on the first acquisition session:

Run #02

Repeat the same operation on the second data file:

Advanced

Saccade SSP

This run #02 is a good example to illustrate how we can use SSP projectors to remove the artifacts caused by eye saccades. You could mark the saccades manually or use the pre-selection available in "1-7Hz".


Note: the event window option will not be used because the events "saccade" are extended events and already include their duration.

Advanced

Elekta-Neuromag SQUID jumps

MEG signals recorded with Elekta-Neuromag systems frequently contain SQUID jumps. They are easy to spot visually in the recordings, they look like sharp steps followed by a change of baseline value. These jumps are due to the instability of the electronics, which fails at controlling the state of the SQUID during the recording sessions.

These steps cause important issues in the analysis of the signal, both in amplitude and in frequency. They are difficult to detect and remove, especially when some pre-processing with the Elekta software has already been applied. Running MaxFilter/SSS on MEG recordings with a SQUID jump on one sensor propagates the artifact to all the sensors.

The best approach is to remove these jumps from the analysis:

An example before MaxFilter (SQUID jump visible on one sensor only):

Examples after MaxFilter (SQUID jump propagated on all the sensors):

Advanced

Additional documentation

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Tutorials/BadSegments (last edited 2019-07-03 17:22:05 by TakfarinasMedani)