Topographic map of sensor correlations

[am3 (Amir Mohammad)]

Hi everyone,

I’m working on analyzing EEG data in Python using MNE and I’d like to create a topographic map visualizing the correlations between different sensors.

I’ve been exploring the MNE documentation, but I’m not quite sure on the best approach.

Could anyone provide some guidance on how to achieve this?

Here’s some additional information that might be helpful:

• Data Format: EEG (122 eeg channel records and 10 ear channels (misc label) )
• Desired Sensor Correlations: Pearson Correlation Coefficient

I’ve successfully calculated the Pearson correlation coefficients for all sensor pairs in my EEG data, resulting in a 132x132 2D NumPy array. Now, I’d like to visualize these correlation values on a topographic map. This map will effectively represent the inter-sensor relationships, allowing me to identify areas with strong or weak correlations between EEG channels. Ideally, the topographic map will utilize a color gradient to depict the correlation strength, with warmer colors indicating positive correlations and cooler colors representing negative correlations.

[mscheltienne (Mathieu Scheltienne)]

A topographic map is simply a representation of an array of shape (n_channels,). Thus, find a metric which yields one value per channel and use mne.viz.plot_topomap to create the topographic representation. The 2 pieces of information needed are (1) the value at each channel location, in the array of shape (n_channels,) and (2) the location of each channels, as a montage in an mne.Info object.

Mathieu