= Tutorial 11: Bad channels [Under construction] = ''Authors: Francois Tadel, Elizabeth Bock, Sylvain Baillet'' It is common during the acquisition to have a few sensors that are recording values that will not be usable in the data analysis. In MEG, a sensor can be damaged or unstable. In EEG, the quality of the connection between the electrode and the subject scalp is sometimes too low to record anything interesting. It is important to identify the sensors with poor signal quality at an early stage of the pre-processing, because the efficiency of the artifact removal will depend on it. If you try to remove blink and cardiac artifacts with some bad sensors it may not work very well, and worse, it will propagate the bad signals to all the channels. This tutorial will explain the various ways we have to handle the bad channels. Note that the recordings from this auditory experiment do not contain any bad sensors, therefore the entire tutorial is optional. If you are not interested, you can skip it and will still be able to follow the next tutorials. <> <> == Identifying bad channels == Some bad channels are easy to detect, their signals look either completely off or totally flat compared with the other surrounding sensors. Some others are more difficult to identify. You may be able to observe that something looks wrong in various ways (the examples below are taken from other datasets): * In the PSD file we always recommend to compute for all the datasets (power spectrum density): <
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> {{attachment:psd_neck.gif||height="146",width="423"}} {{attachment:bad_psd.gif||height="140",width="213"}} * Simply looking at the signals traces, if some channels appear generally noisier than the others: <
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> {{attachment:bad_signal.gif||height="122",width="266"}} * Looking at a 2D sensor topography, if one sensor always has different values from its neighbors: <
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> {{attachment:bad_topo.gif||height="124",width="113"}} * In the source maps, if you see only one region of the cortex activated all the time. <> == Selecting sensors == * Double-click on the recordings for run #01 to open the MEG sensors.<
>Select only one subset of sensor (in the drop-down menu in the Record tab, eg. "CTF LT"). * Right-click on the time series figure > View topography (or press Ctrl+T). * Right-click on the topography figure > Channels > Display sensors (or press Ctrl+E). * If you can't see anything because the topography figure is too small, you can change the way the figures are arranged automatically. In the top-right corner of the Brainstorm figure, select the menu "Window layout options > Tiled". * You can select one channel by clicking on its signal or on the dot representing it in the topography figure. Note that the sensor selection is automatically reported to the other figure. <
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> {{attachment:select_channel.gif||height="152",width="461"}} * You can select multiple sensors at the same time the topography figure. Right-click on the figure, then hold the mouse button and move the mouse. <
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> {{attachment:select_multiple.gif||height="133",width="206"}} * Select a few sensors, then right-click on one of the figures and check ou the Channels menu: <
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> {{attachment:select_popup.gif}} * * '''View selected''': Show the time series of the selected sensors * '''Mark selected as bad''': Remove sensors from the display and all the further computations * '''Mark non-selected as bad''': Keep only the selected channels * '''Reset selection''': Unselect all the selected sensors * '''Mark all channels as good''': Brings back all the channels to display * '''Edit good/bad channels''': Opens an interface that looks like the channel editor, but with one extra column to edit the status (good or bad) of each channel. <> == Marking bad channels == * <> == From the database explorer == == Bad channels == * Select a few channels with one of the method described above: click on the time series, click on the sensors dots, right-click and move to select a group of sensors. Then right-click in one of the figures and check out the '''Channels''' sub-menu: . {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutExploreRecodings?action=AttachFile&do=get&target=channelMenu.gif|channelMenu.gif|class="attachment"}} * '''Mark channels as bad''': Right-click > ''Channels > Mark selected as bad'', or press ''Delete ''key. The sensors should disappear in all figures, and the topography view (2D sensors cap) is updated so that the interpolation on the 2D surface now ignores the bad channels. . {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutExploreRecodings?action=AttachFile&do=get&target=channelSetBad.gif|channelSetBad.gif|class="attachment"}} * '''Get the channels back''': two options * Right-click on figure > ''Channels > Mark all channels as good<
> '' * Right-click on figure ''> Channels > Edit good/bad channels...'' : this menu open a window very similar to the Channel Editor window introduced in previous tutorials, but without the annoying location and orientation values, and with green and red dots. Click on the dots to mark a channel as good or bad. ''' {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutExploreRecodings?action=AttachFile&do=get&target=channelEditGoodBad.gif|channelEditGoodBad.gif|class="attachment"}} ''' * Note that if you click on a row in this window, it will select the corresponding channel in the time series and topography figures. => TO CHECK * Close this window to save the changes. * '''Batching this from the database explorer''': * You will find a "Channels" menu for any node in the tree that contains recordings. * If you do this on the level of a node, the operation will be applied recursively to all the recordings contained in the node. This way, you can quickly set that the channel 63 is always bad, or that electrode 43 is not working for subject #12, without having to visualize all the recordings one after the other. * The'' View all bad channels'' command displays the list of all the bad channels in all the files in the ''Messages ''tab, in main Brainstorm window. {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutExploreRecodings?action=AttachFile&do=get&target=channelTreePopup.gif|channelTreePopup.gif|class="attachment"}} * '''Important notes''': * The good/bad channel flags are stored in the recordings files, not in the channel files. So if you marked some channels as ''bad ''in the ''ERF ''data file, there are still considered as ''good ''in the ''Std ''data file. <> == Montage: Bad channels == <> == Restore the list == == Epoching and averaging == At this stage of the analysis, the database contains only links to raw files. The list of bad channels is saved separately for each dataset. When you import epochs from a continuous file, the list of bad channels will be copied from the raw file to all the imported data files. Then you will be able to redefine this list for each epoch individually, tagging more channels as bad, or including back the ones that are ok. This way it is possible to exclude from the analysis channels that are too noisy in a few trials only, for instance because of some movement artifacts. When averaging, if an epoch contains one bad channel, this bad channel is excluded from the average but all the other channels are kept. It means that not all the channels have the same number of trials for calculating the average. This allows to keep more data in the studies with a low number of trials and lots of noise. <> == On the hard drive == The list of bad channels is saved for each data file separately, in the field '''ChannelFlag'''. This vector indicates for each channel #i if it is good (ChannelFlag(i)=''' 1''') or bad (ChannelFlag(i)=''' -1'''). Right-click on a link to a continuous file > File > View file contents: {{attachment:bad_file.gif}} <> <>