Describe the new feature or enhancement
Dear MNE Community,
I hope this message finds you well. I am currently working with two EEG datasets stemming from the same experimental paradigm, acquired using different caps (biosemi128/64). During my analysis, I encountered a challenge in facilitating a spatial downsampling of the 128-channel montage to enable a meaningful comparison of results between both datasets.
Given that only 34 electrode locations coincide between the two montages, I devised a solution by creating a virtual montage. This involved incorporating the 30 non-matching locations onto the 128-channel head, utilizing spline interpolation to obtain data for these additional points, and subsequently discarding the unwanted channels, then resulting in a 64-channel montage dataset.
While my specific case addresses only biosemi caps, I believe this approach could be valuable in a broader context. It may not be uncommon to compare EEG datasets acquired using different montages. Therefore, I propose the inclusion of a more generalized version of this downsampling method to accommodate various montages.However, perhaps the effectiveness of this enhancement may be limited to montages with electrode locations calculated from a sphere, like the biosemi setup.
I welcome feedback and collaboration from the community to refine and implement this proposed feature.
Describe your proposed implementation
Maybe as a method of DigMontage class.
Describe possible alternatives
Not sure.
Additional context
No response
Describe the new feature or enhancement
Dear MNE Community,
I hope this message finds you well. I am currently working with two EEG datasets stemming from the same experimental paradigm, acquired using different caps (biosemi128/64). During my analysis, I encountered a challenge in facilitating a spatial downsampling of the 128-channel montage to enable a meaningful comparison of results between both datasets.
Given that only 34 electrode locations coincide between the two montages, I devised a solution by creating a virtual montage. This involved incorporating the 30 non-matching locations onto the 128-channel head, utilizing spline interpolation to obtain data for these additional points, and subsequently discarding the unwanted channels, then resulting in a 64-channel montage dataset.
While my specific case addresses only biosemi caps, I believe this approach could be valuable in a broader context. It may not be uncommon to compare EEG datasets acquired using different montages. Therefore, I propose the inclusion of a more generalized version of this downsampling method to accommodate various montages.However, perhaps the effectiveness of this enhancement may be limited to montages with electrode locations calculated from a sphere, like the biosemi setup.
I welcome feedback and collaboration from the community to refine and implement this proposed feature.
Describe your proposed implementation
Maybe as a method of DigMontage class.
Describe possible alternatives
Not sure.
Additional context
No response