mne.preprocessing.compute_bridged_electrodes#

mne.preprocessing.compute_bridged_electrodes(inst, lm_cutoff=16, epoch_threshold=0.5, l_freq=0.5, h_freq=30, epoch_duration=2, bw_method=None, verbose=None)[source]#

Compute bridged EEG electrodes using the intrinsic Hjorth algorithm.

First an electrical distance matrix is computed by taking the pairwise variance. Then, a local maximum near 0 \({\mu}V^2\) and a a local minimum below 5 \({\mu}V^2\) are found, the presence of which is indicative of bridging. Finally, electrode distances below the local minimum are marked as bridged as long as they happen on more than the epoch_threshold proportion of epochs.

Based on [1][2][3] and the EEGLAB implementation.

Parameters:

instinstance of Raw, Epochs or Evoked

The data to compute electrode bridging on.

lm_cutofffloat

The distance in \({\mu}V^2\) cutoff below which to search for a local minimum (lm) indicative of bridging. Defaults to 16 \({\mu}V^2\) to be conservative based on the distributions in [2].

epoch_thresholdfloat

The proportion of epochs with electrical distance less than ed_threshold in order to consider the channel bridged. The default is 0.5.

l_freqfloat

The low cutoff frequency to use. Default is 0.5 Hz.

h_freqfloat

The high cutoff frequency to use. Default is 30 Hz.

epoch_durationfloat

The time in seconds to divide the raw into fixed-length epochs to check for consistent bridging. Only used if inst is mne.io.BaseRaw. The default is 2 seconds.

bw_methodNone

bw_method to pass to scipy.stats.gaussian_kde.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

bridged_idxlist of tuple

The indices of channels marked as bridged with each bridged pair stored as a tuple.

ed_matrixndarray of float, shape (n_epochs, n_channels, n_channels)

The electrical distance matrix for each pair of EEG electrodes.

Notes

New in version 1.1.

References

Examples using mne.preprocessing.compute_bridged_electrodes#

Identify EEG Electrodes Bridged by too much Gel

Identify EEG Electrodes Bridged by too much Gel