Extract electrode location from Brainproducts / Brainvision

Hello all, I am using MNE-Python version 0.23 on a Windows operating system.
I am acquiring data via the LSL Labrecorder (GitHub - labstreaminglayer/App-LabRecorder: An application for streaming one or more LSL streams to disk in XDF file format.) and obtaining therefore the data in an .XDF file format.

I need to access montage files from a Brainproduct device (32 electrodes, LiveAmp amplifier). And by now I might access their montage files only by mne.channels.read_montage (mne.channels.read_montage — MNE 0.15.dev0 documentation).

Our data format is .xdf for EEG data and .rwksp or .bvef for electrode locations.

Unfortunately this is available only in the last version which is not stable.

Which are possible alternative solutions to acess the montage files from such device? Thank everyone in advance!

Hello and welcome,

Could you share the problematic file?
What do you mean by the last version which is not stable? The current stable MNE version is v0.24.1.


Hi Mathieu,

thank you for your timely reply.

Here there are the two files:
.bvef: CLA-32.bvef - Google Drive

.rwksp : CLA-32.bvef - Google Drive

And here is the error I get when try to use mne.ead_montage

mne.channels.read_montage has been deprecated in v0.19. I don’t exactly know what the alternative is, but you could try using: mne.channels.read_custom_montage — MNE 0.24.1 documentation

1 Like

@mscheltienne is correct.

mont = mne.channels.read_custom_montage('CLA-32.bvef')

@FrancescoChiossi, it seems to me like you used an “EasyCap” electrode cap (these are the standard caps that BrainProducts uses for their equipment) with standard 10-20 positions (i.e., you did not record your own electrode locations; let me know if I get this wrong).

I would thus recommend that you use this template: mne-python/easycap-M1.txt at main · mne-tools/mne-python · GitHub

import mne
montage = mne.channels.make_standard_montage(kind="easycap-M1")

# check how it looks

# apply to your data "raw" and see how it looks