mne.read_label#

mne.read_label(filename, subject=None, color=None, *, verbose=None)[source]#

Read FreeSurfer Label file.

Parameters:
filenamestr

Path to label file.

subjectstr | None

Subject which this label belongs to. Should only be specified if it is not specified in the label. It is good practice to set this attribute to avoid combining incompatible labels and SourceEstimates (e.g., ones from other subjects). Note that due to file specification limitations, the subject name isn’t saved to or loaded from files written to disk.

colorNone | matplotlib color

Default label color and alpha (e.g., (1., 0., 0., 1.) for red). Note that due to file specification limitations, the color isn’t saved to or loaded from files written to disk.

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:
labelLabel

Instance of Label object with attributes:

  • comment: comment from the first line of the label file

  • vertices: vertex indices (0 based, column 1)

  • pos: locations in meters (columns 2 - 4 divided by 1000)

  • values: values at the vertices (column 5)

Examples using mne.read_label#

Generate simulated evoked data

Generate simulated evoked data

Compute Power Spectral Density of inverse solution from single epochs

Compute Power Spectral Density of inverse solution from single epochs

Compute power and phase lock in label of the source space

Compute power and phase lock in label of the source space

Compute source power spectral density (PSD) in a label

Compute source power spectral density (PSD) in a label

Compute MNE-dSPM inverse solution on single epochs

Compute MNE-dSPM inverse solution on single epochs

Compute sLORETA inverse solution on raw data

Compute sLORETA inverse solution on raw data

Extracting time course from source_estimate object

Extracting time course from source_estimate object

Extracting the time series of activations in a label

Extracting the time series of activations in a label