Neuropype Version: 2019.1.2

Fixes/Changes:

  • Updated visualization plot nodes (Bar, TimeSeries, TopoPlot).
  • Updated sample pipelines.
  • Bugfix in ROIActivation.
  • Documentation links point to new location under neuropype.io/docs

Neuropype Version: 2019.1.1

Fixes/Changes:

  • Fix to resolve scikit-learn package compatibility issue.

Neuropype Version: 2019.1.0

New Nodes:

  • Diagnostics package:
  • ChannelDiagnostics: Generates plots showing various quality metrics (streaming data)
  • CorrelationMetric: Computes a channel correlation signal QA metric
  • HighFrequencyNoise: Computes a high frequency noise signal QA metric
  • LineNoise: Computes a line noise signal QA metric
  • ElementWiseMath package:
  • Discretize: Convert continuous data into discrete values using multi-level thresholding
  • FileSystem: package:
  • ExportCSV: Export signal data to CSV format
  • Visualization package:
  • MarkerStreamWindow: Display a window showing markers as they are received in realtime (for QA)
  • ScrollPlot: Plot multi-channel continuous time-series data in a scrollable plot (for offline data)
  • TopoPlotViewer: Plot EEG electrodes on a 2D head colored based on incoming data (i.e., a metric)

Pipelines:

  • Signal Quality Assessment: Displays various plots showing realtime signal quality

Fixes/Changes:

  • OverrideAxis: fixed case where running this node would change the axis order
  • ExportEDF: writing > 1 marker/sec optional as not supported by all EDF import libraries
  • ImportBrainvision: fixed formatting of event marker stream
  • Fixed issue that caused Windows not to find python.exe in some cases
  • Minor bugfixes

Other:

  • Neuropype QuickLaunch: (Run Pipeline ...): support for enum and bool parameter inputs, parameter validity check, graphic makeover, etc.

Neuropype Version: 2019.0.1

  • Minor bugfixes, including issue with launch on Windows under certain circumstances

Neuropype Version: 2019.0.0

New Nodes:

  • Cardiac package:
  • HeartRate: calculates BPM from R peaks
  • HeartRateVariability: calculates HRV from R peaks
  • RDetection: detect R peaks in QRS complexes in ECG time series and peaks in PPG time series and signals
  • Connectivity package:
  • PhaseLockingValue: calculates the phase-locking value between all pairs of channels
  • FeatureExtraction package:
  • FactorAnalyis: perform Factor Analysis of given data
  • TensorDecomposition: decompose a tensor into a number of rank-1 tensors
  • FileSystem package:
  • Export nodes for JSON, MAT, YAML formats
  • Import nodes for Axon, BCI2000, BrainVision, NSX, Neuralynx, NeuroExplorer, PLX, Spike2, and TDT file formats
  • PathIterator: powerful iterator over a list of paths names, captures metadata, accepts wildcards, or a study manifest file
  • SkipIfExists: skip file path if checked path already exists
  • Formatting package:
  • GetAxisMask: get data slices that meet criteria parameters and output the result as a mask packet
  • ApplyAxisMask: apply axis mask created with GetAxisMask
  • SeparateSignalChunks: separate signal from non-signal chunks (streams)
  • Markers package:
  • RemoveIntersectingMarkerSpans: remove spans of markers that intersect signal boundaries
  • AddEventMarker: find trials matching a defined set of event markers and add a new marker matching one of those events
  • ExportMarkers: export all markers found in data to CSV list
  • RewriteMarkers: rewrite event markers according to mapping rules (supports regex)
  • Neural package:
  • ArtifactDetection: detect artifacts in a sliding window fashion and mark them up with NaNs
  • FixSignalUnit: infer and then correct the unit of an electrophysiological signal
  • RemoveOutlierTrials: remove trials with abnormally large signal values
  • CreateList: create a list from a series of input values
  • Passthrough: pass data through when update flag is set
  • StringParse: parse a string using a pattern with placeholders in it
  • SignalProcessing package:
  • ApplyLinearTransformation: calculate the dot product of the data tensor with a filters tensor
  • FixGaps: repair data gaps in a time series
  • MedianFilter: calculate a sliding-window median over the data
  • PeakFinding: find all peaks in the given data along a given axis using a continous wavelet method
  • QuantileStandardization: standardize data by converting into quantiles relative to a sliding window
  • RobustRectangularStandardization: robustly standardize the signal in a sliding rectangular window
  • ShiftedWindows: extract overlapped windows from the given time series
  • SignalWhitening: perform a whitening (sphering) transform of the signal
  • UnitNormalization: normalize the data along an axis
  • Spectral package:
  • ContinuousWaveletTransform: calculate a spectrogram (periodogram) of the data using the Continuous Wavelet Transform (CWT)
  • CurveLength: compute the curve length of the data over a given axis
  • GroupedMean: group instances based on given criteria then calculate the mean (and error) for each group
  • RootMeanSquare: compute the root mean square over a given axis
  • TTest: calculate a t-test (1-sample or 2-sample unpaired and paired t-tests)
  • TTestTwoInput: calculate a t-test from two inputs (1-sample and 2-sample unpaired and paired t-tests)
  • Winsorize: winsorize the data
  • TensorMath package:
  • Operate: perform a mathematical operation on all data between pairs of elements along an axis
  • StripSingletonAxis: strip the given singleton axis from the data
  • __Diagnostics__package:
  • CopyData: copy the given data
  • RandomMatrix: create a packet of random data
  • CountAlongAxis: count number of instances along an axis
  • DiscardNaNChunks: discard chunks with NaNs
  • ExtractChunkProperty: extract a given property from one of the chunks of a packet
  • OverrideAxisLabel: override the label of an axis
  • OverrideProperties: override a meta-data property of a chunk
  • RepeatAlongAxis: repeat the data along a given axis
  • StripMetadata: strip undesired meta-data from a packet
  • Visualization package:
  • BarPlotPG: plot EEG channels as a bar plot

Resources

  • egi-gsn-hydrocell-257.locs: montage for EGI Hydrocel GSN

Major Engine Changes:

  • Caching of intermediate results
  • Instance axis can have any number of custom per-element fields
  • graph.connect() precedence rules changed if port not given
  • Graph now has high-level load/save operations
  • Scripting: nodes that accept input1..N can now be given a list of data packets
  • Improved workflow for writing nodes that make use of other nodes
  • New migrate parameter in ports that can be used to migrate legacy values for backward compatibility
  • Scripting: scripts involving nodes now support full auto-complete for node names and their arguments (this is aided by the new refresh_nodes script)
  • iPython included with Neuropype to enable Jupyter notebook workflow!

Node Improvements and Bugfixes:

  • Too many to list!