Module: connectivity
Connectivity estimation algorithms.
These are domainspecific nodes that estimate informationflow measures between multiple signal channels of a time series, with a focus on EEG.
ComplexCoherence
Estimate the Complex Coherence (Coh) measure from a previously computed MVAR model.
Like the other nodes computing dynamical measures, this node accepts as input a multivariate autoregressive (MVAR) model, or, more commonly, a time series of such models. Such models are typically estimated using a modelfitting 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 4way tensor as output). The desired frequencies can be selected via a parameter.
Version 1.0.0
Ports/Properties

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
Userdefinable metadata associated with the node. verbose name: Metadata
 default value: {}
 port type: DictPort
 value type: dict (can be None)

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 halfopen Pythonstyle 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)

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 oneelement (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 dynamicalmeasure nodes can be concatenated along the feature axis, and the resulting tensor will retain the names of the measures in its resulting (multielement) feature axis. verbose name: Include Feature Axis
 default value: False
 port type: BoolPort
 value type: bool (can be None)

remove_auto_connections
Set autoconnectivity estimates to zero. verbose name: Mask Auto Connections
 default value: False
 port type: BoolPort
 value type: bool (can be None)
ComplexSpectralDensity
Estimate the Complex Spectral Density measure from a previously computed MVAR model.
Like the other nodes computing dynamical measures, this node accepts as input a multivariate autoregressive (MVAR) model, or, more commonly, a time series of such models. Such models are typically estimated using a modelfitting 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 4way tensor as output). The desired frequencies can be selected via a parameter.
Version 1.0.0
Ports/Properties

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
Userdefinable metadata associated with the node. verbose name: Metadata
 default value: {}
 port type: DictPort
 value type: dict (can be None)

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 halfopen Pythonstyle 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)

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 oneelement (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 dynamicalmeasure nodes can be concatenated along the feature axis, and the resulting tensor will retain the names of the measures in its resulting (multielement) feature axis. verbose name: Include Feature Axis
 default value: False
 port type: BoolPort
 value type: bool (can be None)

remove_auto_connections
Set autoconnectivity estimates to zero. verbose name: Mask Auto Connections
 default value: False
 port type: BoolPort
 value type: bool (can be None)
DirectDirectedTransferFunction
Estimate the Direct Directed Transfer Function ( dDTF) or shorttime dDTF (sdDTF) measure from a previously computed MVAR model.
Like the other nodes computing dynamical measures, this node accepts as input a multivariate autoregressive ( MVAR) model, or, more commonly, a time series of such models. Such models are typically estimated using a modelfitting 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 4way tensor as output). The desired frequencies can be selected via a parameter. In addition to dDTF, this node can also compute the shorttime dDTF (sdDTF) using the normalization parameter.
Version 1.0.0
Ports/Properties

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
Userdefinable metadata associated with the node. verbose name: Metadata
 default value: {}
 port type: DictPort
 value type: dict (can be None)

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 halfopen Pythonstyle 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)

temporal_normalization
Calculate shorttime dDTF (sdDTF). This is enabled using shorttime temporal normalization. verbose name: Temporal 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 oneelement (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 dynamicalmeasure nodes can be concatenated along the feature axis, and the resulting tensor will retain the names of the measures in its resulting (multielement) feature axis. verbose name: Include Feature Axis
 default value: False
 port type: BoolPort
 value type: bool (can be None)

remove_auto_connections
Set autoconnectivity estimates to zero. verbose name: Mask Auto Connections
 default value: False
 port type: BoolPort
 value type: bool (can be None)
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 multivariate autoregressive (MVAR) model, or, more commonly, a time series of such models. Such models are typically estimated using a modelfitting 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 4way 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.
Version 1.0.0
Ports/Properties

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
Userdefinable metadata associated with the node. verbose name: Metadata
 default value: {}
 port type: DictPort
 value type: dict (can be None)

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 halfopen Pythonstyle 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 oneelement (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 dynamicalmeasure nodes can be concatenated along the feature axis, and the resulting tensor will retain the names of the measures in its resulting (multielement) feature axis. verbose name: Include Feature Axis
 default value: False
 port type: BoolPort
 value type: bool (can be None)

remove_auto_connections
Set autoconnectivity estimates to zero. verbose name: Mask Auto Connections
 default value: False
 port type: BoolPort
 value type: bool (can be None)
Efficiency
Calculate the (global or local) efficiency parameters of a weighteddirected network.
it is assumed that the network weights are in two space axes of the input packet. The average of inverse shortest path length is the global efficiency which is inversely related to the characteristic path length. The local efficiency is similar to the global efficiency but computed on the neighborhood of the node. References: Latora and Marchiori (2001) Phys Rev Lett 87:198701. Onnela et al. (2005) Phys Rev E 71:065103 Fagiolo (2007) Phys Rev E 76:026107. Rubinov M, Sporns O (2010) NeuroImage 52:105969 Wang Y et al. (2016) Neural Comput 21:119.
Version 0.0.1
Ports/Properties

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
Userdefinable metadata associated with the node. verbose name: Metadata
 default value: {}
 port type: DictPort
 value type: dict (can be None)

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

type
The type of efficiency to be computed, it can be either 'global' of 'local'. verbose name: Type Of Efficiency
 default value:
 port type: StringPort
 value type: str (can be None)

normalize
Normalize the weight matrix if this is True. verbose name: Normalize
 default value: False
 port type: BoolPort
 value type: bool (can be None)
GeneralizedPartialDirectedCoherence
Estimate the Generalized Partial Directed Coherence (GPDC) measure from a previously computed MVAR model.
Like the other nodes computing dynamical measures, this node accepts as input a multivariate autoregressive (MVAR) model, or, more commonly, a time series of such models. Such models are typically estimated using a modelfitting 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 4way tensor as output). The desired frequencies can be selected via a parameter.
Version 1.0.0
Ports/Properties

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
Userdefinable metadata associated with the node. verbose name: Metadata
 default value: {}
 port type: DictPort
 value type: dict (can be None)

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 halfopen Pythonstyle 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)

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 oneelement (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 dynamicalmeasure nodes can be concatenated along the feature axis, and the resulting tensor will retain the names of the measures in its resulting (multielement) feature axis. verbose name: Include Feature Axis
 default value: False
 port type: BoolPort
 value type: bool (can be None)

remove_auto_connections
Set autoconnectivity estimates to zero. verbose name: Mask Auto Connections
 default value: False
 port type: BoolPort
 value type: bool (can be None)
GrangerGewekeCausality
Estimate the Granger Geweke Causality (GGC) measure from a previously computed MVAR model.
Like the other nodes computing dynamical measures, this node accepts as input a multivariate autoregressive (MVAR) model, or, more commonly, a time series of such models. Such models are typically estimated using a modelfitting 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 4way tensor as output). The desired frequencies can be selected via a parameter.
Version 1.0.0
Ports/Properties

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
Userdefinable metadata associated with the node. verbose name: Metadata
 default value: {}
 port type: DictPort
 value type: dict (can be None)

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 halfopen Pythonstyle 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)

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 oneelement (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 dynamicalmeasure nodes can be concatenated along the feature axis, and the resulting tensor will retain the names of the measures in its resulting (multielement) feature axis. verbose name: Include Feature Axis
 default value: False
 port type: BoolPort
 value type: bool (can be None)

remove_auto_connections
Set autoconnectivity estimates to zero. verbose name: Mask Auto Connections
 default value: False
 port type: BoolPort
 value type: bool (can be None)
GroupLassoMVAR
Fit an adaptive multivariate autoregressive model (MVAR) using a group LASSO regression approach.
MVAR modeling is the first step in estimating connectivity (information flow or other dependencies between signals) from EEG, usually followed by estimation of one the available dynamical measures (e.g., dDTF or PDC). This node accepts a multichannel time series, and will, in a sliding window, estimate an MVAR solution for each time point. Usually, the series of produced models will have a significantly lower sampling rate as the original time series (controlled by the sliding window step size). In order to adaptively fit MVAR models on short windows of data, regularization or control of the complexity of the solution is required, and this algorithm implements a type of regularization that leads to solutions that are more sparse, that is, have fewer interactions between signals than nonsparse models. This type of regularization is therefore also called sparsity, and its strength can be controlled using a parameter. This method is fundamentally capable of realtime operation if there are not too many channels in the input signal and the sampling rate is not too high. This node offers a variety of parameters that, while not changing the solution, offer tuning opportunities for expert users to reach that solution in fewer iterations and thus more rapidly. Please refer to the parameters and associated tooltips for more details. However, the baseline performance of this method with default settings should be usable for a wide range of typical EEG settings on 2 to 15 channels. While the method is relatively robust to settings and is not prone to producing spurious solutions due to false "local minima", there are possible failure scenarios, which can, for instance, manifest in a solution that is nonsparse, or allzero. These issues can almost always be addressed by better choice of the various tuning parameters. Note that estimating connectivity on scalp channels, while technically not a problem, has interpretation issues due to the high likelihood of finding spurious associations between channels due to volume conduction smearing the brain activity out across channels. For this reason, this node is best on signals that better reflect the activation of individual brain sources or source areas. The most commonly used setup is to use a sourcelocalization node such as sLORETA or LCMV beamforming (see documentation of the respective nodes for their correct use), followed by estimation of activity in regions of interest and possible merging or selection of the relevant regions, which also helps with reducing the number of input channels to this node to a reasonable number.
Version 1.1.0
Ports/Properties

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
Userdefinable metadata associated with the node. verbose name: Metadata
 default value: {}
 port type: DictPort
 value type: dict (can be None)

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

window_length
Length of the sliding estimation windows (in seconds). Determines how many samples go into fitting the MVAR model for a given time range. Larger values will give better estimates, but at the cost of less precision in time, since the resulting sequence of MVAR models will change more slowly (smoothly) over time. verbose name: Sliding Window Length
 default value: 0.5
 port type: FloatPort
 value type: float (can be None)

window_step
Sliding window step size. The amount, in seconds, by which the sliding window is shifted to estimate the next MVAR model. Larger values give a coarser output time series, but will be computed faster. Very small step sizes compared to the window length (e.g., less than 10% of the length) will yield little benefit, since the MVAR models for two highly overlapped windows will be very similar. The largest reasonable value is 1/2 of the window length, and if this value is larger than the window length, input data samples will be skipped entirely. verbose name: Sliding Window Step Size
 default value: 0.1
 port type: FloatPort
 value type: float (can be None)

window_func
Window function to apply to sliding window. Optionally the data in the sliding window can be tapered using a window function to emphasize the signal in the center of the window. A simple and wellbehaved window is the Hann window. Any nondefault window will, however, require a larger window length to attain the same effective sample size (and thus estimation quality) as the default rectangular window. verbose name: Window Function
 default value: rect
 port type: EnumPort
 value type: object (can be None)

window_param
Parameter for window function. Some of the window functions are parametric, and in those cases this parameter must be specified. Needed for kaiser, gaussian, slepian, and chebwin. verbose name: Window Function Parameter
 default value: None
 port type: FloatPort
 value type: float (can be None)

model_order
MVAR model order. This is the number of 'taps' that the MVAR model uses, i.e., how many past samples it uses to predict the signal at the current sample. A good value is in the 1015 range. Model of higher orders are harder to estimate well, and require either more data (larger window length), or stronger regularization, resulting in a more sparse model, i.e., models with fewer nonzero connections. verbose name: Model Order
 default value: 10
 port type: IntPort
 value type: int (can be None)

normalize_columns
Normalize columns of predictor and response matrices. verbose name: Normalize Columns
 default value: False
 port type: BoolPort
 value type: bool (can be None)

lambda_reg
Regularization strength. This parameter governs how sparse the solution is assumed to be, where larger values yield a more sparse result. Sparse means that the connectivity solution will have few nonzero edges. Stronger regularization makes it possible to fit a more complex model (more parameters), or use less data (e.g., shorter sliding window) to fit a model of same complexity. verbose name: Regularization Strength
 default value: 0.1
 port type: FloatPort
 value type: float (can be None)

max_iter
Maximum number of iterations. This is used to limit for how many iterations the MVAR model fitting procedure runs to fit a single time window. On wellbehaved data where the solution does not change dramatically from one sliding window to the next, the solver will typically terminate in relatively few iterations (perhaps 10 to 100) without hitting this limit, although this can vary depending on the other settings. A larger number will ensure that the solver can run to full convergence, but at the risk that it may much longer to solve for certain time windows than for others. For realtime use a good choice is around 100 to guarantee predictable performance. verbose name: Max Iterations
 default value: 100
 port type: IntPort
 value type: int (can be None)

abs_tolerance
Absolute convergence tolerance. Smaller values will lead the solver run to a closer approximation of the optimal solution, at the cost of increased running time. See also relative tolerance. verbose name: Absolute Tolerance
 default value: 0.0001
 port type: FloatPort
 value type: float (can be None)

rel_tolerance
Relative convergence tolerance. Smaller values will lead the solver run to a closer approximation of the optimal solution, at the cost of increased running time. In contrast to the absolute tolerance, this value is relative to the magnitude of the regression weights. Note that the used method works best when the desired accuracy is not excessive, and merely a good approximation is sought. verbose name: Relative Tolerance
 default value: 0.01
 port type: FloatPort
 value type: float (can be None)

verbose
Produce verbose output. verbose name: Verbose
 default value: False
 port type: BoolPort
 value type: bool (can be None)

rho
Initial value of augmented Lagrangian parameter. This parameter, which is specific to the used solver, can be autotuned, which makes the method is relatively robust to the initial value. However, slight adjustments in the 0.1 to 10 range can reduce the number of iterations required for convergence and thereby the running time. However, choosing a grossly inappropriate value can cause the method to fail to converge, which is easily diagnosed by having a solution that is essentially nonsparse. verbose name: Solver Rho
 default value: 1.0
 port type: FloatPort
 value type: float (can be None)

alpha
Overrelaxation parameter (alpha). Like rho, this parameter is a highly technical detail of the used solver. A value in the default range is known to modestly improve the timetosolution of the algorithm in many cases compared to setting the value to 1.0, which effectively disables overrelaxation. verbose name: Solver Alpha
 default value: 1.7
 port type: FloatPort
 value type: float (can be None)

rho_update
Autotune rho parameter. Whether to update the solver's rho parameter dynamically. This can be used to achieve faster convergence times in highly timesensitive setups, but there is a modest risk that on some data the solution can 'blow up', although this could potentially be overcome by tuning the other solver parameters related to the rho update logic. verbose name: AutoTune Solver Rho
 default value: True
 port type: BoolPort
 value type: bool (can be None)

rho_cutoff
Rho update trigger threshold. This determines how frequently updates to the solver rho parameter can be triggered. A larger value will lead to rho changing less frequently. This parameter is essentially a tradeoff between the solver adapting more quickly to settings that are optimal for convergence (using a lower value) versus preventing settings from changing too erratically (using a higher value) and thus prompting stalls or convergence failures on difficult data. verbose name: Solver Rho Update Threshold
 default value: 10.0
 port type: FloatPort
 value type: float (can be None)

rho_incr
Rho update increment factor. When rho is being increased, this is the factor by which it is changed. Larger values can lead to quicker adaptation if the initial value is off or solutions change rapidly, at an increased risk of overshooting. verbose name: Solver Rho Increment Factor
 default value: 2.0
 port type: FloatPort
 value type: float (can be None)

rho_decr
Rho update decrement factor. When rho is being decreased, this is the factor by which it is divided. Larger values can lead to quicker adaptation if the initial value is off or solutions change rapidly, at an increased risk of overshooting. verbose name: Solver Rho Decrement Factor
 default value: 2.0
 port type: FloatPort
 value type: float (can be None)

lambda_update
Autotune regularization strength. Whether to adjust the regularization strength dynamically if convergence is slow. This is a somewhat experimental feature, but it can reduce the need to adjust the lambda parameter by hand. verbose name: AutoTune Regularization Strength
 default value: False
 port type: BoolPort
 value type: bool (can be None)

lambda_update_thresh
Threshold for triggering regularization strength (lambda) updates. Only in effect if autotuning for this parameter is enabled. Larger values make it less likely that an update is triggered spuriously. verbose name: Lambda Update Threshold
 default value: 1e05
 port type: FloatPort
 value type: float (can be None)

lambda_update_count
Number of iterations before updating lambda. Update lambda convergence has not significantly improved after this many iterations. verbose name: Lambda Update Count
 default value: 10
 port type: IntPort
 value type: int (can be None)

lambda_update_factor
Update factor by which to divide lambda. verbose name: Lambda Update Factor
 default value: 10
 port type: FloatPort
 value type: float (can be None)
ImaginaryCoherence
Estimate the Imaginary Coherence (iCoh) measure from a previously computed MVAR model.
Like the other nodes computing dynamical measures, this node accepts as input a multivariate autoregressive (MVAR) model, or, more commonly, a time series of such models. Such models are typically estimated using a modelfitting 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 4way tensor as output). The desired frequencies can be selected via a parameter.
Version 1.0.0
Ports/Properties

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
Userdefinable metadata associated with the node. verbose name: Metadata
 default value: {}
 port type: DictPort
 value type: dict (can be None)

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 halfopen Pythonstyle 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)

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 oneelement (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 dynamicalmeasure nodes can be concatenated along the feature axis, and the resulting tensor will retain the names of the measures in its resulting (multielement) feature axis. verbose name: Include Feature Axis
 default value: False
 port type: BoolPort
 value type: bool (can be None)

remove_auto_connections
Set autoconnectivity estimates to zero. verbose name: Mask Auto Connections
 default value: False
 port type: BoolPort
 value type: bool (can be None)
MultipleCoherence
Estimate the Multiple Coherence (mCoh) measure from a previously computed MVAR model.
Like the other nodes computing dynamical measures, this node accepts as input a multivariate autoregressive (MVAR) model, or, more commonly, a time series of such models. Such models are typically estimated using a modelfitting 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 4way tensor as output). The desired frequencies can be selected via a parameter.
Version 1.0.0
Ports/Properties

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
Userdefinable metadata associated with the node. verbose name: Metadata
 default value: {}
 port type: DictPort
 value type: dict (can be None)

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 halfopen Pythonstyle 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)

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 oneelement (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 dynamicalmeasure nodes can be concatenated along the feature axis, and the resulting tensor will retain the names of the measures in its resulting (multielement) feature axis. verbose name: Include Feature Axis
 default value: False
 port type: BoolPort
 value type: bool (can be None)

remove_auto_connections
Set autoconnectivity estimates to zero. verbose name: Mask Auto Connections
 default value: False
 port type: BoolPort
 value type: bool (can be None)
PartialCoherence
Estimate the Partial Coherence (pCoh) measure from a previously computed MVAR model.
Like the other nodes computing dynamical measures, this node accepts as input a multivariate autoregressive (MVAR) model, or, more commonly, a time series of such models. Such models are typically estimated using a modelfitting 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 4way tensor as output). The desired frequencies can be selected via a parameter.
Version 1.0.0
Ports/Properties

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
Userdefinable metadata associated with the node. verbose name: Metadata
 default value: {}
 port type: DictPort
 value type: dict (can be None)

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 halfopen Pythonstyle 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)

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 oneelement (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 dynamicalmeasure nodes can be concatenated along the feature axis, and the resulting tensor will retain the names of the measures in its resulting (multielement) feature axis. verbose name: Include Feature Axis
 default value: False
 port type: BoolPort
 value type: bool (can be None)

remove_auto_connections
Set autoconnectivity estimates to zero. verbose name: Mask Auto Connections
 default value: False
 port type: BoolPort
 value type: bool (can be None)
PartialDirectedCoherence
Estimate the Partial Directed Coherence (PDC) or normalized PDC measure from a previously computed MVAR model.
Like the other nodes computing dynamical measures, this node accepts as input a multivariate autoregressive (MVAR) model, or, more commonly, a time series of such models. Such models are typically estimated using a modelfitting 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 4way tensor as output). The desired frequencies can be selected via a parameter. In addition to PDC, this node can also compute the normalized PDC (nPDC) using the normalization parameter.
Version 1.0.0
Ports/Properties

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
Userdefinable metadata associated with the node. verbose name: Metadata
 default value: {}
 port type: DictPort
 value type: dict (can be None)

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 halfopen Pythonstyle 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 PDC (nPDC). 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 oneelement (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 dynamicalmeasure nodes can be concatenated along the feature axis, and the resulting tensor will retain the names of the measures in its resulting (multielement) feature axis. verbose name: Include Feature Axis
 default value: False
 port type: BoolPort
 value type: bool (can be None)

remove_auto_connections
Set autoconnectivity estimates to zero. verbose name: Mask Auto Connections
 default value: False
 port type: BoolPort
 value type: bool (can be None)
PhaseLockingValue
Calculate the phaselocking value between all pairs of channels.
This assumes that the phases have already been computed, e.g., using the FilterBank node followed by the ComplexPhase node.
Version 0.1.0
Ports/Properties

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
Userdefinable metadata associated with the node. verbose name: Metadata
 default value: {}
 port type: DictPort
 value type: dict (can be None)

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