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OverlappedLinearModel

Estimate a linear model with temporally overlapped responses.

The model applies a GLM that explains the observed signal as a sum of stimulus-locked responses, (according one of the selectable response models) for different stimulus instance types, plus optional time-varying nuisance regressors from selectable signal chunks, plus noise. The model controls for the false discovery rate due to temporally correlated observations by spectrally whitening the data, and for artifacts by using a robust (iteratively reweighted least squares) estimator. When using the FIR model, the response is estimated in time-series form, and when using the Canonical HRF model, the magnitude of the modeled response is estimated (i.e., not temporally resolved). More Info... Version 0.9.0

Ports/Properties

data

Data to process.

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

response_model

Evoked response model. This is the model of any event-locked responses expected in the data. FIR will estimate the response as a time series. Canonical HRF will estimate the amplitude of a canonical hemodynamic response function. The response is time-locked to the onset and duration of instances in a marker stream.

  • verbose name: Response Model
  • default value: fir
  • port type: EnumPort
  • value type: str (can be None)

response_parameters

Parameter overrides for the response model, given as a Python dictionary. The parameters are per the documentation of the CanonicalHRF and FIRHRF models, respectively. In brief. FIRHRF supports numBins, binWidth, binDelay, and timeUnit parameters, and CanonicalHRF supports

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

max_autocorr_delay

Estimate auto-correlations up to at most this delay (in seconds). This translates to the maximum model order of an autoregressive model that is being estimated alongside the linear response model. The actual model order may be lower when it is estimated from the data. If set to 0, no autocorrelations are accounted for.

  • verbose name: Max Autocorrelations Delay
  • default value: 4
  • port type: FloatPort
  • value type: float (can be None)

robust

Use robust estimation for linear model. This uses an iteratively reweighted least-squares (IRLS) method using the Tukey Bisquare scale function.

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

regressor_streams

Streams with time-domain nuisance or auxiliary regressors. This can be additional streams that carry explanatory variables that shall be regressed out of the data as part of the model, for instance auxiliary measures of artifacts, or certain kinds of low-order trend terms etc (e.g., via the node AppendSyntheticChannels, which can add such a stream).

  • verbose name: Regressor Streams
  • default value: ['synthetic']
  • port type: ListPort
  • value type: list (can be None)

statistic_axis

Output descriptive statistics along an axis of this type.

  • verbose name: Statistic Axis
  • default value: feature
  • port type: EnumPort
  • value type: str (can be None)

dependent_streams

List of stream names to use as dependent variables (default: all remaining).

  • verbose name: Dependent Streams
  • default value: None
  • port type: ListPort
  • value type: list (can be None)

max_iter

Max number of outer-loop (AR refit) iterations in optimization.

  • verbose name: Max Iterations
  • default value: 10
  • port type: IntPort
  • value type: int (can be None)

max_iter_inner

Max number of inner-loop (IRLS) iterations in optimization.

  • verbose name: Max Iterations (Inner)
  • default value: 20
  • port type: IntPort
  • value type: int (can be None)

reltol

Max relative error during estimation.

  • verbose name: Max Relative Tolerance
  • default value: 0.01
  • port type: FloatPort
  • value type: float (can be None)

use_caching

Enable caching.

  • verbose name: Use Caching
  • default value: True
  • port type: BoolPort
  • value type: bool (can be None)

num_procs

Number of parallel processes to use. The maximum would be the number of channels in the data. If given as -1, this will use one process per core on the system, and if given as None, this will use as many processes as there are cores in the system, divided by OMP_NUM_THREADS (or 4 if not present).

  • verbose name: Num Procs
  • default value: 1
  • port type: IntPort
  • value type: int (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)