Package: signal_processing

Signal processing algorithms.

These are generic signal-processing methods that are largely independent of the specific modality of the data (e.g., EEG, eye tracking, and so on). Most of these nodes require that the inbound data has a time axis, and some require that the data also has a space axis (i.e., channels). Most nodes in this category are stateful and support seamless processing of streaming data; many furthermore guarantee that the concatenated outputs on successive parts of a time series equals the output of the same node on the entire time series at once (we call such nodes "chunkable"). Additionally, some nodes will adapt themselves to the data they receive, either on a configurable initial portion (which they will buffer in a streaming fashion or select from the beginning of offline data) or the whole data. Such statistics will typically be reset on each subsequent packet received that is not marked as "streaming" (is_streaming property), and the nodes can additionally be reset through other neuropype mechanisms (e.g., usage in a function body, or for each loop body, where by default state is not preserved), or relying on NeuroPype's reset propagation that is triggered by some nodes such as Import nodes upon emitting new data.

Nodes in this package:

• Add Noise
  Add noise to the given data.

• Align Streams
  Aligns multiple streams in the time domain.

• AlignWindows
  Aligns signals in two data packets.

• Apply Linear Transformation
  Calculates a dot product or matrix multiply of the data tensor with a filters tensor.

• Artifact Regression
  Remove artifacts by regression from data using specified reference channels.

• Center of Mass
  Find the center of mass or centroid along an axis.

• Channel Baseline Calibration
  This node can be used to "zero" an output signal based on some initial baseline calibration data.

• Channel Subset
  Select a subset of channels to retain.

• Combine Channels
  Combine the given axis elements into new axis elements.

• Decimate
  Decimate (subsample) the signal by an integer factor.

• Delay Embedding
  Perform delay embedding of the given signal.

• Delay Signal
  Delay signal by a given number of samples.

• Detect Line Noise Frequency
  Detects the line noise (mains) frequency of raw signal data, as a float.

• Detrending
  Remove trends from the given data.

• Exponential Moving Average
  Smooth the given data using an exponentially decaying window.

• Exponential Standardization
  Standardize the given data using an exponentially decaying window.

• FIR Filter
  Apply FIR filter to signal.

• Fix Gaps
  Fix up data gaps in time series.

• Frequency Normalization
  Correct for 1/f frequency falloff.

• Guess Sampling Rate
  Finds the estimated sampling rate of streamed data.

• IIR Filter
  Apply IIR filter to signal.

• Incremental Whitening
  Perform a whitening (sphering) transform of the signal incrementally.

• Infomax Independent Component Analysis
  Compute an ICA solution on calibration data and subsequently transform data into component space.

• Interpolation
  Interpolate the given data along a desired axis.

• Interpolate Gaps
  Finds and interpolate gaps in data.

• M Sequence
  Generates m sequence time series for the given order (m).

• Median Filter
  Calculate a sliding-window median over the data.

• Moving Average
  Smooth the signal using a moving average.

• Moving Window
  Output a moving window of a streaming time series.

• Peak Amplitude
  Find the amplitude of the tallest peak along an axis.

• Peak Finding (Contour-Based)
  Find all peaks in the given data along a given axis.

• Peak Finding (Wavelet-based)
  Find all peaks in the given data along a given axis.

• Peak Latency
  Find the latency or offset of the tallest peak along an axis.

• Prune Flatline Channels
  Prune channels that flat-line for extended periods of time in the calibration data.

• Quantile Standardization
  Standardize data by converting into quantiles relative to a sliding window.

• Rectangular Moving Window Standardization
  Standardize the signal in a sliding rectangular window.

• Reference Signals
  Generates sinusoidal reference signals with given frequencies.

• Re-referencing
  Subtract the average of some reference data.

• Resample
  Resample the signal to a different sampling rate.

• Robust Rectangular Standardization
  Robustly standardize the signal in a sliding rectangular window.

• Savitzky-Golay Filter
  Apply a Savitzky-Golay filter to an array.

• Shifted / Overlapped Windows
  Extract overlapped windows from the given time series.

• Signal Whitening
  Perform a whitening (sphering) transform of the signal.

• Spectrally Whiten Time Series
  IIR filter a time series to produce a flat spectrum.

• TRCA
  Perform Task-Related Component Analysis (TRCA) of the input data.

• UnitNormalization
  Normalize the data along an axis by dividing each data point by the sum of all data points (within each row), or the sum of the squares of all data points.

• Window Function
  Apply a window function to the given data along some axis.