ANTNeuro Waveguard 32 channels montage issue

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1
  • MNE version: 1.5.1
  • operating system: Windows 10

I am using ANTNeuro system with waveguard 32 channels electrode cap.
the electrode channels include :-
[‘Fp1’, ‘Fpz’, ‘Fp2’, ‘F7’, ‘F3’, ‘Fz’, ‘F4’, ‘F8’, ‘FC5’, ‘FC1’, ‘FC2’, ‘FC6’, ‘M1’, ‘T7’, ‘C3’, ‘Cz’, ‘C4’, ‘T8’, ‘M2’, ‘CP5’, ‘CP1’, ‘CP2’, ‘CP6’, ‘P7’, ‘P3’, ‘Pz’, ‘P4’, ‘P8’, ‘POz’, ‘O1’, ‘Oz’, ‘O2’]

I am trying to set standard 1020 montage on my data, however the electrode mapping doesnt seems correct.
standard1020

I tried to compare the electrode positioning using other montages. in most of the cases there were several channels which not present in the montages.
However, only M1 and M2 were missing in case of Biosemi 64, Easycap-M1 and brainproduct-RNP-BA-128.

Just to see the issue, i removed M1 and M2 from my data and used brainproduct-RNP-BA-128 montage. this time it was much better than standard 1020…

mont Brain prod RNP
mont Brain prod RNP978×814 57.2 KB

Here, the dotted shows the default electrode layout of waveguard cap. and the solid shows the brainproduct montage.
But i cannot use this montage as it doesn’t contains two channels available in my data.

Code:

P_Num = 2
Myfile= r’C:\Users\Data A\P{}\Session-1\Data\P{}-Ss1-X1-eeg.edf’.format(P_Num, P_Num)
raw=mne.io.read_raw_edf(Myfile, preload=True)

#The default names of channles were like :- ‘EEG Fp1-CPz’ … So renaming them
channel_mapping = {
‘EEG Fp1-CPz’: ‘Fp1’,
‘EEG Fpz-CPz’: ‘Fpz’,
‘EEG Fp2-CPz’: ‘Fp2’, - - - - - - - }

raw.rename_channels(channel_mapping)

standard_montage = mne.channels.make_standard_montage(‘standard_1020’)
raw.set_montage(standard_montage)
raw.plot_sensors(show_names=True)

Can you please help me with the accurate montage that i can use with my data.

2

Hello @meharahsanawais and welcome to the forum!

The standard_* montages in MNE are designed such that they immediately work when doing source localization with the fsaverage brain. This is why they look a little different from what you might expect.

You had the correct idea choosing the easycap or biosemi montages for sensor-level analysis. What you need to do first, though, is simply rename the M1 and M2 channels in your data to TP9 and TP10, respectively. (See Using contralateral referencing for EEG — MNE 1.5.1 documentation where they go the opposite route)

Then, you can set your montage:

raw.set_montage("easycap-M1")  # or any other template montage

When plotting the montage, pass sphere="eeglab" and everything should look like you expect.

Best wishes,
Richard

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