Yokogawa/KIT tutorial: Median nerve stimulation

Authors: Francois Tadel, Yasuhiro Haruta, Ei-ichi Okumura, Takashi Asakawa

This tutorial introduces some concepts that are specific to the management of MEG/EEG files recorded with Yokogawa/KIT systems in the Brainstorm environment.

Note that the operations used here are not detailed, the goal of this tutorial is not to introduce Brainstorm to new users. For in-depth explanations of the interface and theoretical foundations, please refer to the introduction tutorials.

License

This tutorial dataset (MEG/EEG and MRI data) remains proprietary of Yokogawa Electric Corporation, Japan. Its use and transfer outside the Brainstorm tutorial, e.g. for research purposes, is prohibited without written consent from Yokogawa Electric Corporation.

Description of the experiment

This tutorial is based on a simple median nerve stimulation experiment:

• Right median nerves were percutaneously stimulated using monophasic square-wave impulses with a duration of 0.3 ms at 2.8 Hz.
• The stimulus intensity was set at the motor threshold to evoke mild twitches of the thumb.
• The stimulus onsets were recorded as low-to-high TTL with a trigger channel labeled as "Trigger01".
• The total number of stimuli in the dataset was 336.
• The MEG data was recorded with a sampling rate of 2000 Hz and a bandpass filter at 0.16-500 Hz with a Yokogawa 160 axial gradiometer system at Yokogawa Electric Corporation, Kanazawa, Japan.
• The EEG data was recorded with a NIHON KOHDEN system simultaneously with the MEG recordings.

Export recordings from Meg160

To import Yokogawa/KIT data files (.con, .raw, .ave) into Brainstorm, a data export process is required beforehand. The data export function is available in Meg160, which is data analysis software equipped in most of Yokogawa/KIT systems.

The dataset used in this tutorial has already been exported using this procedure. It is described here so that later you can export your own recordings to Brainstorm.

If your software does not support the functions used below, please contact Yokogawa via
http://www.yokogawa.com/me/index.htm

Export the digitizer file

• If a data file and the corresponding digitizer file are ready for use, there is no additional operation required.
• If no digitizer file is available, you need first to extract the head surface points:

  • In Meg160, select the menu: File > Import and Export > BESA Text Export > Surface Point File

  • Check that the fiducial points are properly pointed, and then click the [OK] button
  • A surface point file (.sfp) is automatically created. It includes the position data of: the fiducial points, the marker points and other points describing the head shape

Full head shape in the the digitizer file

In order to realize a precise MRI registration or for warping the default anatomy, you should collect 100 to 200 points describing the entire head shape in addition to the 8 Yokogawa/KIT standard stylus points. To import additional digitized points, follow the instruction below:

• When digitizing head points:
  • Pick the 8 standard stylus points
  • Pick additional 100 to 200 head points, so that the selected points cover the entire head

• Edit the digitizer label file (DigitizeLabel.txt) which is used in "Third-party export" so that it defines the 8 points and the additional points.

  • Note that the number of additional labels should be >= the number of digitized points.

  • As for the new label names, any names can be used if the names are not duplicated.
  • The following is an example:

      fidt9
      HPI_1
      HPI_4
      HPI_3
      HPI_5
      HPI_2
      fidt10
      fidnz
      ETC001
      ETC002
      ETC003
      ...
      ETC[nnn]

  • Where [nnn]+8 should be >= the number of digitized points.

• Use the modified digitizer label file with the digitizer data in the "Third-party export" function.

Export the recordings

• In Meg160, select the menu: File > Import and Export > Third-Party Export

  • On some systems, this menu is name [BESA Binary Export]
  • An operation panel for data export shows up
• When using digitizer data:
  • Select [Digitizer]

  • Enter the digitizer file in the [Point Filename] box
    (.txt file generally available under the corresponding "Scan" folder)

  • Enter the label file in the [Label Filename] box
    (DigitizeLabel.txt generally located in the "C:\Meg160\AppInfo" folder)

• When using surface point data instead:
  • Select [Surface Point File]
  • Enter the surface point file (.sfp) created previously.
• Enter an output file name in [Third-party Export Dataset]
• Click on [Create Export File]
• Use this exported file in Brainstorm

Alternative

Some older versions of the Yokogawa/KIT software do not inlcude this export menu "Third-party export". In this case, you can pass the digitizer information to Brainstorm with three additional files, saved in the same folder as the .sqd file containing the MEG recordings you want to process. The folder must contain one file only for each type of information, therefore you cannot save multiple runs or subjects in the same folder, you must create one subfolder per acquisition run.

• *_Marker1_*: File with extension .mrk or .sqd with the HPI coils in MEG device coordinates

• *_Points.txt: Polhemus FastSCAN file with the fiducials and HPI coils in digitizer coordinates (mm)

• *_HS.txt: Polhemus FastSCAN file with the head shape points in digitizer coordinates (mm).

Download and installation

• Requirements: You have already followed all the introduction tutorials and you have a working copy of Brainstorm installed on your computer.

• Go to the Download page of this website, and download the file: sample_yokogawa.zip

• Unzip it in a folder that is not in any of the Brainstorm folders (program or database folder)

• Start Brainstorm (Matlab scripts or stand-alone version)

• Select the menu File > Create new protocol. Name it "TutorialYokogawa" and select the options:

  • "No, use individual anatomy",

  • "No, use one channel file per condition".

Import the anatomy

• Right-click on the TutorialYokogawa folder > New subject > Subject01

  • Leave the default options you set for the protocol

• Right-click on the subject node > Import anatomy folder:

  • Set the file format: "FreeSurfer folder"

  • Select the folder: sample_yokogawa/anatomy

  • Number of vertices of the cortex surface: 15000 (default value)

• Click on the link "Click here to compute MNI transformation".

• Set the 6 required fiducial points (indicated in MRI coordinates):
  • NAS: x=128, y=227, z=93
  • LPA: x=48, y=130, z=69
  • RPA: x=214, y=130, z=76

• At the end of the process, make sure that the file "cortex_15000V" is selected (downsampled pial surface, which will be used for the source estimation). If it is not, double-click on it to select it as the default cortex surface.
  
  

Access the recordings

Link the recordings

• Switch to the "functional data" view, the middle button in the toolbar above the database explorer.

• Right-click on the subject folder > Review raw file:

  • Select the file format: "MEG/EEG : Yokogawa/KIT"

  • Select the file: sample_yokogawa/data/SEF_000-export.con

• Answer NO when asked to refine the registration using head points. In this dataset, we only have access to the positions of the electrodes and three additional markers on the forehead. The automatic registration doesn't work well in this case, we are going to fix this registration manually.

• A figure is opened to show the current registration MRI/MEG. It is already quite good, but can be improved a bit manually. Close this figure.
• The new file "Link to raw file" lets you access directly the contents of the MEG/EEG recordings
• The channel file "KIT channels" contains the name of the channels and the position of the corresponding sensors.

  
  

Prepare the channel file

• The recordings contain signals coming from different types of electrodes:
  • 160 MEG channels
  • 12 MEG references
  • 14 Trigger channels
  • 41 EEG channels
  • 2 EOG channels: EO1 and EO2
  • 1 ECG channel: EKG+
  • 1 ground of the EEG amplifier: E
• Not all the types of channels are properly identified in Brainstorm. We need to redefine this manually to get correct groups of sensors.

• Right-click on the channel file > Edit channel file:

  • Channel EO1 (208) and EO2 (209): Change the type to EOG

  • Channel EKG+ (214): Change the type to ECG

  • Channel E (231): Change the type to MISC

  • Close the figure and accept to save the modifications

  
  

Refine the MRI registration

• Right-click on the channel file > MRI registration > Edit... (EEG)

• The white points are the electrodes, the green points are the additional digitized head points. To display the label of the electrodes, click on the [LABEL] button in the toolbar. To see what the other buttons in the toolbar are doing and how to use them, leave your mouse over them for a few seconds and read the description.

• Now try to manipulate the position of the EEG+MEG sensors using rotations and translations only (no "resize" or individual electrodes adjustments). The objective is to have all the points close to the surface and the three forehead points inside the little peaks on the surface (due to markers in the MRI).

• The rotation+translation are going to be applied both to the EEG and the MEG sensors. After you are done with this solid registration part, you can click on the button "Project electrodes on scalp surface", it will help for the source modeling later. The green points (digitized) stay in place, the white points (electrodes) are now projected on the skin of the subject.

• If you feel like you didn't do this correctly, close the figure and cancel the modifications, then try again. It takes a few trials to get used to this rotation/translation interface.

• Click on [OK] when done.

  • Answer YES to save the modifications.

  • Answer YES again to apply the solid transformation (rotation+translation) to the MEG sensors.

• Before manual registration:
  
  

• After manual registration:
  
  

Read the stimulation information

• Right-click on the "Link to raw file" > Trigger > Display time series

• In the Record tab, switch to a column view of the sensors (first button in the toolbar)

• You can see that all the trigger lines are flat except for "Trigger01", which contains the information of the electric stimulation. We are going to read this trigger channel as a list of events.
  
  

• In the Record tab, menu File > Read events from channel.

  • Event channel = Trigger01

  • Option selected "TTL": detect peaks of 5V/12V on an analog channel.

  • Do not select the option Accept zeros as trigger values
    
    

• Check that the peaks of the triggers channel have been correctly identified, then close this figure.
  
  

Pre-processing

Evaluate the recordings

• Drag and drop the "Link to raw file" into the Process1 list.

• Select the process "Frequency > Power spectrum density", configure it as follows:
  
  

• Double-click on the new PSD file to display it.
  
  

• The lines on the top represent the EEG electrodes, the lines at the bottom the MEG sensors. If you want to get clearer plots, you can calculate separately the spectrum for the two types of sensors separately, by running twice the process "Power spectrum density" , once with sensor types = "MEG" and once with "EEG", instead of running in on both at the same time like we did.
• Observations (below 250Hz):
  • Peak around 11Hz: Alpha waves from the subject's brain
  • Peaks at 60Hz, 120Hz, 180Hz, 240Hz on EEG + MEG: Power lines (60Hz+harmonics)
  • Peaks at 35Hz, 65Hz, 70Hz, 183Hz, 197Hz on MEG only: Noise coming from an unknown source

  • MEG sensor LC11 (in red) appears to have a higher level of noise than all the other MEG sensors, we will check this when review the MEG recordings and probably tag it as a bad channel.

Frequency filters

• Yokogawa software: Users can apply digital filtering to the data before exporting them to Brainstorm. Depending on the data size and degrees of filter parameters, it may take a long time.

• Brainstorm: You could correct for the 60Hz+harmonics peaks with a notch filter, as presented in the introduction tutorials: Power spectrum and frequency filters

• For now we are going to keep the file as it is. There is no important contamination below 60Hz and we are going to study processes that occur at lower frequencies. When studying evoked responses, the electric noise at 60Hz tends to cancel out and is usually not a problem in the analysis. Note for later that it is however important to consider this electric noise when working on resting state recordings or time-frequency decompositions at higher frequencies.

Review the recordings

MEG: Default montages

Pre-defined selections of sensors are available to help reviewing the MEG recordings.

• Right-click on the "Link to raw file" > MEG > Display time series.

• Display the channels in columns (first button in the toolbar of the Record tab).
• Check the list of montages available for this file: click on the drop-down menu in the Record tab.

• Select the montage "KIT LC"
  
  

If you don't see all the "KIT..." entries in this list, load them manually:

• Click on All > Edit montages...

• Click on the [Load montage] button
• Go to the folder: brainstorm3/toolbox/sensors/private
• Select the file format "MNE selection files (*.sel)"

• Select the file mne_montage_yokogawa.sel

• Click on save to close the montage editor.
  
  

MEG: Bad channels

• Click on the noisy LC11 sensor to select it (displayed in red)

• Press the delete key or right-click in the figure > Channels > Mark selected as bad.
  
  

• Close this figure

EEG: Bad channels

• Right-click on the "Link to raw file" > EEG > Display time series.

• Check the list of available EEG montages.
  
  

• In the Record tab, increase the length of the displayed time window to 10s.

• You will see that channel TP9 is behaving in a strange way. Select it and mark it as bad.
  
  

EEG: Average reference

• Right-click on the "Link to raw file" > EEG > Display time series.

• In the Record tab, menu Artifacts > Re-reference EEG > "AVERAGE".
  
  

• At the end, the window "select active projectors" is open to show the new re-referencing projector. Just close this window. To get it back, use the menu Artifacts > Select active projectors.

Artifacts detection

MEG: Heartbeats and blinks

• Select the "Link to raw file" in the Process1 tab, then create the following analysis pipeline

• Process "Events > Detect heartbeat" on channel EKG+

• Process "Events > Detect eye blinks" on channel EO2 (the signal is better than EO1)

• Process "Events > Remove simultaneous": Remove "cardiac" when too close to "blink", 250ms.

• Process "Artifacts > SSP: Heartbeats" for MEG

• Process "Artifacts > SSP: Eye blinks" for MEG

• Run the pipeline.
  
  

• Double-click on the "Link to raw file" to show the MEG sensors.

• In the Record tab, menu Artifacts > Select active projectors
  
  

• By default, only the first components is selected. This is an arbitrary selection that does not work all the time. You should always inspect manually all the spatial components you remove from your recordings, to avoid removing any of the data of interest. We are now going to edit the default selections for these four categories.

• Blink: MEG: Select component #1
  
  

• Cardiac: MEG: Select component #1
  
  

• Click on [Save] to keep your modifications.

Epoching and averaging

Import recordings

In this experiment, the electric stimulation is sent with a frequency of 2.8Hz, meaning that the inter-stimulus interval is 357ms. We are going to import epochs of 300ms around the stimulation events.

• Right-click on the Link to raw file > Import in database:

  • Select the entire time definition (0s to 120s, default)

  • Check "Use events" and select "Trigger01"

  • Epoch time: [-50, 250] ms

  • Check "Apply SSP" (make sure that it shows 4 active categories / 4 total projectors)
    

  • Check "Remove DC offset" > Time range > [-50, -10] ms
    
    

• At the end, you are asked whether you want to ignore one epoch that is shorter than the others. This happens because the acquisition of the MEG signals was stopped less than 250ms after the last stimulus trigger was sent. Therefore, the last epoch cannot have the full [-50,250]ms time definition. This shorter epoch would prevent us from averaging all the trials easily. As we already have enough repetitions in this experiment, we can just ignore it.
  Answer Yes to this question to discard the last epoch.

Average epochs

• Drag and drop all the Trigger01 trials to the Process1 tab.

• Run the process "Average > Average files".
  
  

• Review the average for the MEG and the EEG.
  
  

Source analysis

Head model

• We are going to use a realistic head model, this requires to calculate some additional surfaces for the subject, to represent the inner skull and outer skull surface. Go to the "Anatomy" view, right-click on the subject > Generate BEM surfaces.

• Use 1922 vertices for each layer (default).
  
  

• Go back to the "Functional data" view, right-click on the channel file > Compute head model.
  
  

• Leave all the default options for the head model (cortex surface, MEG=Overlapping, EEG=OpenMEEG). Then leave all the OpenMEEG options to their defaults except for one: select the option "Use adjoint formulation".
  
  

• If the automatic download doesn't work, download OpenMEEG and install it manually (menu Help).

• If the OpenMEEG calculation crashes, please refer to the OpenMEEG tutorial.

• If you cannot get OpenMEEG to work, or if the results definitely do not make sense, try using a different forward model: "3-shell sphere". It's a spherical model, so it would perform better in the regions of the head that are close to the sphere. See the Head model tutorial.

Noise covariance matrix

• We will use the baseline of the single epochs to calculate the noise covariance matrix.

• Right-click on the Trigger01 epochs group > Noise covariance > Compute from recordings.
  
  

• Enter the same baseline interval we used for removing the DC offset: [-50, -10] ms
  
  

Inverse model

• Right-click on the head model > Compute sources.

• Select wMNE and MEG. When asked, leave the default list of bad channels (31: channel LC11)
  
  

• Repeat the same operation for EEG. It is better to study separately the two modalities because the method for combining MEG and EEG are not working well yet.
  
  

• This operation creates a shared inversion kernel and one source link for each block of recordings in the folder. If you are not familiar with those concepts, please refer to the Source estimation tutorial.
  
  

• Display the sources for the MEG (top) and the EEG (bottom).
  

Z-score normalization

• A good way to reveal better the source activity at the cortex level is to calculate a Z-score of the source maps with respect with a quiet baseline. We can use the same baseline as for the calculation of the noise covariance matrix.
• Drag and drop both MEG and EEG average sources in Process1

• Run the process: Standardize > Z-score (dynamic), with the baseline [-50,-10]ms

• Double-click on the new file to display it
  
  

Regions of interest

• Create two scouts S1 and S2 to represent the primary and secondary somatosensory cortex of the left hemisphere.
  • Open an average source file (eg. MEG Z-score) and the corresponding recordings
  • Go to 20ms, adapt the amplitude threshold and the colormap to see only a focal source
  • Create a scout at the center of the activated region
  • Grow the scout to about 20 vertices and rename it to "S1"
  • Go to 50ms and repeat the same operation for S2. In this specific case, S2 does not appear a clear independent region, as we still see stronger activities in more superior regions of the brain. So pick one activated region around the anatomical location of the secondary somatosensory cortex (at the very bottom of the post-central gyrus). See the picture below.

  • For more information on the scouts, please refer to the scouts tutorial.

• Then plot the activity for the different files we calculated.
  
  

• MEG sources, wMNE (left) and Z-score (right):
  
  

• EEG sources, wMNE (left) and Z-score (right):
  
  

Scripting

The following script from the Brainstorm distribution reproduces the analysis presented in this tutorial page: brainstorm3/toolbox/script/tutorial_yokogawa.m