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)

Compute bridged EEG electrodes using the intrinsic Hjorth algorithm.

First, an electrical distance matrix is computed by taking the pairwise variance between electrodes. Local minimums in this matrix below lm_cutoff are indicative of bridging between a pair of electrodes. Pairs of electrodes are marked as bridged as long as their electrical distance is below lm_cutoff on more than the epoch_threshold proportion of epochs.

Based on 123 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. EEGLAB defaults to 5 \({\mu}V^2\). MNE defaults to 16 \({\mu}V^2\) to be conservative based on the distributions in Greischar et al.2.

epoch_thresholdfloat

The proportion of epochs with electrical distance less than lm_cutoff 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 v1.1.

References

1

C. E. Tenke and J. Kayser. A convenient method for detecting electrolyte bridges in multichannel electroencephalogram and event-related potential recordings. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 112(3):545–550, March 2001. doi:10.1016/s1388-2457(00)00553-8.

2(1,2)

Lawrence L. Greischar, Cory A. Burghy, Carien M. van Reekum, Daren C. Jackson, Diego A. Pizzagalli, Corrina Mueller, and Richard J. Davidson. Effects of electrode density and electrolyte spreading in dense array electroencephalographic recording. Clinical Neurophysiology, 115(3):710–720, March 2004. doi:10.1016/j.clinph.2003.10.028.

3

Arnaud Delorme and Scott Makeig. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1):9–21, March 2004. doi:10.1016/j.jneumeth.2003.10.009.

Examples using mne.preprocessing.compute_bridged_electrodes

Identify EEG Electrodes Bridged by too much Gel

Identify EEG Electrodes Bridged by too much Gel