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DirectedTransferFunction

Estimate the Directed Transfer Function (DTF) or normalized DTF measure from a previously computed MVAR model.

Like the other nodes computing dynamical measures, this node accepts as input a multi-variate auto-regressive (MVAR) model, or, more commonly, a time series of such models. Such models are typically estimated using a model-fitting node such as the Group LASSO MVAR node, the output of which can be directly used by this node (see documentation of that node for proper usage). The output of this node is a square matrix that quantifies connectivity between pairs of brain sources for each frequency bin of interest, and possibly for each time point if the input data were a time series (then yielding a 4-way tensor as output). The desired frequencies can be selected via a parameter. In addition to DTF, this node can also compute the normalized DTF (nDTF) using the normalization parameter. More Info... 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

frequencies

Frequencies over which to compute the measure, in Hz. This is a list of frequencies (e.g., [1,2,3,4]), or a range expression such as 1...4, or a half-open Python-style range such as 1:5. The node will output one connectivity matrix for each frequency, formatted as a tensor.

  • verbose name: Frequencies
  • default value: 1...15
  • port type: Port
  • value type: object (can be None)

normalization

Calculate normalized DTF (nDTF).

  • verbose name: Normalization
  • default value: False
  • port type: BoolPort
  • value type: bool (can be None)

absolute_value_squared

Compute squared magnitude of complex values. If disabled the raw complex values will be returned, i.e., including some phase information.

  • verbose name: Absolute Value Squared
  • default value: True
  • port type: BoolPort
  • value type: bool (can be None)

include_feature_axis

Include a feature axis. If enabled, a one-element (dummy) feature axis will be appended that has the name of this measure set as the name of the feature. This way, the output of multiple dynamical-measure nodes can be concatenated along the feature axis, and the resulting tensor will retain the names of the measures in its resulting (multi-element) feature axis.

  • verbose name: Include Feature Axis
  • default value: False
  • port type: BoolPort
  • value type: bool (can be None)

remove_auto_connections

Set auto-connectivity estimates to zero.

  • verbose name: Mask Auto Connections
  • default value: False
  • port type: BoolPort
  • value type: bool (can be None)

denominator_epsilon

Small value to add to denominators to avoid division by zero.

  • verbose name: Denominator Epsilon
  • default value: 0
  • port type: FloatPort
  • value type: float (can be None)

regularization

Regularization parameter to add to matrices that need to be inverted.

  • verbose name: Regularization
  • default value: 0
  • port type: FloatPort
  • value type: float (can be None)

time_reversed

Compute time-reversed connectivity. This is a simple analytical reversal unless two MVAR solutions (one forward, one backward are provided).

  • verbose name: Time Reversed
  • 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)