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Package: diagnostics

Diagnostic output.

These nodes allow you to print diagnostics and debug your pipelines or your code, and offer feature such as sanity-checking the data, or printing node properties.

Nodes in this package:

  • Add Sample Counter
    Add a sample counter channel to the signal.

  • Alert
    Shows and/or sounds an alert if incoming values are a 1 (ON), otherwise does nothing (this node does not pass data through).

  • Assert Equal
    Assert that the given data is equal to the configured value throw an error otherwise.

  • Assert Signals are Identical
    Ensure that the two input signals have identical data.

  • Assert Monotonic Time
    Assert that the time stamps in the given data are monotonically increasing.

  • Assert No Dropouts
    Assert that the given data's time stamps contain no dropouts.

  • Assert No Duplicate Chunks
    Check for (and optionally remove) successive duplicate chunks from the data.

  • Channel Diagnostics
    This node calls several other nodes, such as LineNoise, CorrelationMetric and HighFrequencyNoise, to compute and display those metrics as topoplots, along with a single weighted "Bad Channel" signal quality metric also displayed as a topoplot.

  • Channel Quality (Good/Ok/Bad) Classification
    This node inputs several other nodes, such as LineNoise, CorrelationMetric and HighFrequencyNoise, with provided classification cutoff values to threshold each metric and provide a final weighted "Bad Channel Classification" signal quality metric displayed as a single topoplot and/or output data packet as classification values 1, 2, or 3 (good, ok, bad).

  • Channel Impedance
    Compute per-channel impedance for EEG data having an impedance carrier wave.

  • Copy Data (Diagnostic)
    Copy the given data.

  • Correlation Metric (Meta)
    A meta-node that repairs bad channels and evaluates the correlation of each channel to its neighbors.

  • Data Counter
    Counts data passing through the node in a given stream, and prints a log message every n seconds (configurable) with the number of samples received (along the time axis), since last check as well as total so far.

  • Auto-detect EEG Reference Channels
    Automated detection of EEG channel(s) being used as a reference.

  • EEQ Signal Quality Score
    Automated EEG quality scoring per channel based on weighted quality metrics.

  • EEG Quality Metrics
    Computes quality metrics for an EEG dataset/file (average for the entire dataset/file).

  • Estimate Lag
    Estimate the lag (delay) of the data in seconds, as computed by the difference between the timestamp of the incoming data and the LSL or wallclock time it was received.

  • Estimate Timeshift
    Estimate the time shift between two streams in the given data, in seconds.

  • Estimate Update Rate
    Estimate the update rate of NeuroPype.

  • Gate Check
    Check that the data packet meets certain criteria before allowing it to pass through.

  • Get Data Identifier
    Get a diagnostic identifier (string) for the given data, if available.

  • Has NaN Or Inf
    Check if the given data structure contains NaN or infinite values.

  • High Frequency Noise (Meta)
    A meta-node that obtains a ratio of high frequencies (e.g

  • Is Debug Mode
    Determine if the pipeline is running in debug mode.

  • Line Noise (Meta)
    A meta-node that performs a standard line noise chain to compute potential line noise contamination (for diagnostics).

  • Low Frequency Noise (Meta)
    A meta-node that obtains a ratio of low frequencies (e.g

  • NirsIntensityQualityMetrics
    Compute various quality metrics from NIRS "raw" Intensity signal data.

  • Nirs Optical Density Quality Metrics
    Computes various quality metrics from NIRS data that has been converted to optical density.

  • No Data Alert
    Alert if no data is present.

  • Pretty-Print Table
    Pretty-print tabular data to the console.

  • Print Packet To Console
    Print the given data to the console.

  • Random Matrix
    Generate an artificial packet with a random matrix.

  • Signal To Noise Ratio
    Signal to noise, computed as the log ratio of signal (filtered) and noise (unfiltered - filtered) data segments, with a baseline removed.

  • With Profiler
    Run the given computational graph with profiling enabled.