I’m configuring a GUI using tkinter. Is that possible to configure laplacian (local average reference) in mne EEG. It is good dealing at focal epilepsy or other focal lesions, for instance, focal vascular, cortical focal displasias, etc. It really “cleans” garbage from signal.
If not:
I was wandering if is possible, for example, to create a ‘lar’ virtual reference for, lets say, F7, by doing—> (Fp1+F3+T3) /3, and keeping channels Fp1 F3 T3 still plotting. Should I use instead —> (Fp1+F3 +F7+T3) / 3, to get a reference for F7. I could do the same for
all channels. By the way would it be correct, could I call it lar reference?
Or,
Do you know any algorithm tha I could adapt to mne EEg to build Lar
Among other fonts I’ve read 'The Surface Laplacian Technique in EEG: Theory and Methods(1)
but couldn’t figure it out.
MNE currently only supports average or single/multi-electrode references (mne.set_eeg_reference()) and bipolar references (mne.set_bipolar_reference()). Local average reference (aka Laplacian) would need to take channel neighborhoods into account, including the desired number of neighboring electrodes.
I’d like to try, but Im a physician not a mathematician. Laplacian reference is importan to clinicians. It helps to focus localized generators as epileptic foci. If anyone know where can I gather info to build it, would be of help . I work with 20 electrodes, for instance, reference for T3 is just the mean of (F7+ C3+ T5) microvolts/3 ? I cant believe it could be so ease. ‘‘what’s the catch’’ in buinding of this so important reference? best. PKanda
Coming up with something sufficiently generic to integrate in MNE-Python is probably a decent amount of work. However, you could relatively quickly hack something that works just for your use case. It is probably easiest if you work directly with the NumPy array holding the EEG data (raw.get_data()) . That way, you could create a new RawArray from scratch by computing the Laplace derivations for each channel manually, exactly as you suggested.
@PauloKanda if you have any additional info to add (additional papers showing the benefits of local average referencing, any parameters that you will need to be able to control case-by-case, etc) please add them there.