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SourcePowerComodulation

Extract signal components whose variance is is maximally correlated to a target variable of interest.

This filter can be used as an adaptive preprocessing step for a multichannel signal, such as EEG, EMG, or MEG, whose variance shall subsequently be used in a regression setup (e.g., to predict some continuous target variable, for instance some cognitive state). This node will calibrate itself if it receives a non-streaming (offline) chunk that has a time, space, and instance axis, and which has a target value for each instance (similarly to how machine learning nodes operate). Note that SPoC should be preceded by a bandpass filter (e.g., FIR or IIR) that restricts the signal to the frequency band of interest. This method can also be thought of as the canonical generalization of CSP from binary classification to regression. Tip: a continuous time series with markers can be segmented into multiple labeled trials / segments using the Assign Target Markers node followed by the Segmentation node. 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

nof

Number of spatial pattern pairs to compute. This determines the number of output channels (which is 2x this value) and thus the dimensionality of the feature space. Typical values are 2-4; while one can generate more features (up to the number of input channels), these will be increasingly less useful to the classifier.

  • verbose name: Number Of Pattern Pairs
  • default value: 3
  • port type: IntPort
  • value type: int (can be None)

cov_lambda

Covariance regularization parameter. This parameter (between 0 and 1) controls the amount of shrinkage regularization applied to the covariance matrix estimates. Usually, only a small amount is necessary to prevent degenerate solutions, e.g., when channels are linearly dependent.

  • verbose name: Covariance Regularization Parameter
  • default value: 0.001
  • port type: FloatPort
  • value type: float (can be None)

initialize_once

Do not recalibrate on subsequent offline chunks, even if they include target labels. If False, this node will recalibrate itself on any offline chunk that has data plus target labels.

  • verbose name: Calibrate Only Once
  • default value: True
  • port type: BoolPort
  • value type: bool (can be None)

target_field

The name of the instance data field that contains the target variable to be correlated. This parameter will be ignored if the packet has previously been processed by a DescribeStatisticalDesign node.

  • verbose name: Target Field
  • default value: TargetValue
  • port type: StringPort
  • value type: str (can be None)

verbose

Produce verbose output.

  • verbose name: Verbose
  • 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)