- MneExperiment.make_bad_channels_neighbor_correlation(r, epoch=None, add_bads=True, save=True, **state)
Iteratively exclude bad channels based on low average neighbor-correlation
float) – Minimum admissible neighbor correlation. Any channel whose average correlation with its neighbors is below this value is added to the list of bad channels (e.g., 0.3).
bool) – Reject bad channels first.
Save the bad channels to the bad channel specification file. Set
save=Falseto examine the result without actually changing the
... – State parameters.
- Return type
neighbor_correlation – Head-map with the neighbor correlation for each sensor.
bad_channels – Channels that are excluded based on criteria.
Load the corresponding data
Calculate the pairwise correlation between each neighboring sensor pair
Assign to each sensor the average correlation with its neighbors
If the sensor with the lowest correlation is <
r, exclude it and go back to 2.
Data is loaded for the currently specified
rawsetting, but bad channels apply to all
rawsettings equally. Hence, when using this method with multiple subjects, it is important to set
rawto the same value.