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SLORETA

Estimate brain source activity using the Standardized Low-Resolution Brain Electromagnetic Tomography (sLORETA) algorithm.

This node accepts an EEG time series, and estimates for each time point the current source density (CSD) for a large number of locations (e.g., a few 1000) in the brain. The result is a time series with the EEG channels replaced by as many channels as there are source locations, and otherwise the same format as the input data. The set of source locations is defined by a headmodel, which is selectable via the headmodel_file parameter. Like eLORETA, this is a rather easy-to-use and very fast source estimation method. Important: the input data to this node should previously have been re-referenced to common average reference using the ReReferencing node (with default settings). Note: by default, the headmodel will be matched to EEG channel space using the EEG channel names, which are then assumed to be in the 10-20 (or the finer-grained 10-5) system. If your EEG channels do not use this label system, you need to know the locations of your channels relative to the 10-20 locations (which can be found in the resources/montages/standard-10-5-* file), and using that, you can either try to replace the channel labels by the closest 10-5 equivalent labels, or, likely better, select the interpolate option in this node to directly interpolate the head model for your given channel locations. Version 1.0.0

Ports/Properties

data

Data to process.

  • verbose name: Data
  • default value: None
  • port type: DataPort
  • value type: Packet (can be None)
  • data direction: INOUT

headmodel

Head model file to use (*.h m). If omitted, the node checks if the data has a current head model associated with it, e.g., because a previous node has annotated it, and then uses that head model. If a relative path is given, the file is searched in the resources/headmodels folder of the cpe.

  • verbose name: Head Model File
  • default value:
  • port type: StringPort
  • value type: str (can be None)

regu

Regularization strength. Larger values yield smoother solutions. In theory this is proportional to the sensor noise level. Ad-hoc choices work fairly well for visualization and basic source localization.

  • verbose name: Regularization Strength
  • default value: 0.05
  • port type: FloatPort
  • value type: float (can be None)

interpolate

Rely on channel locations rather than labels. If this is enabled, the head model will be matched to the data using the channel locations rather than channel labels. This is only useful if the labels are unrecognized (e.g., non-standard labels), and if there are high-quality measured channel locations available. These locations must be in the same coordinate system as NeuroPype's default 10-5 locations (see node documentation for more details).

  • verbose name: Interpolate Using Channel Locations
  • default value: False
  • port type: BoolPort
  • value type: bool (can be None)

set_breakpoint

Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.

  • verbose name: Set Breakpoint (Debug Only)
  • default value: False
  • port type: BoolPort
  • value type: bool (can be None)

metadata

User-definable meta-data associated with the node. Usually reserved for technical purposes.

  • verbose name: Metadata
  • default value: {}
  • port type: DictPort
  • value type: dict (can be None)