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 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 belowlm_cutoff
on more than theepoch_threshold
proportion of epochs.Based on [1][2][3] and the EEGLAB implementation.
- Parameters:
- instinstance of
Raw
,Epochs
orEvoked
The data to compute electrode bridging on.
- lm_cutoff
float
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_threshold
float
The proportion of epochs with electrical distance less than
lm_cutoff
in order to consider the channel bridged. The default is 0.5.- l_freq
float
The low cutoff frequency to use. Default is 0.5 Hz.
- h_freq
float
The high cutoff frequency to use. Default is 30 Hz.
- epoch_duration
float
The time in seconds to divide the raw into fixed-length epochs to check for consistent bridging. Only used if
inst
ismne.io.BaseRaw
. The default is 2 seconds.- bw_method
None
bw_method
to pass toscipy.stats.gaussian_kde
.- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- instinstance of
- Returns:
Notes
New in version 1.1.
References
Examples using mne.preprocessing.compute_bridged_electrodes
#
Identify EEG Electrodes Bridged by too much Gel