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NEMO ERP Analysis Toolkit ERP Pattern Segmentation

NEMO ERP Analysis Toolkit ERP Pattern Segmentation. An Overview. NEMO Information Processing Pipelin e. NEMO Information Processing Pipelin e Pattern Decomposition Component. NEMO Information Processing Pipeline ERP Pattern Segmentation, Identification and Labeling.

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NEMO ERP Analysis Toolkit ERP Pattern Segmentation

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  1. NEMO ERP Analysis ToolkitERP Pattern Segmentation An Overview

  2. NEMO Information Processing Pipeline

  3. NEMO Information Processing PipelinePattern Decomposition Component

  4. NEMO Information Processing PipelineERP Pattern Segmentation, Identification and Labeling • Obtain ERP data sets with compatible functional constraints • NEMO consortium data • Decompose / segment ERP data into discrete spatio-temporal patterns • ERP Pattern Decomposition/ ERP Pattern Segmentation • Mark-up patterns with theirspatial, temporal & functional characteristics • ERP Metric Extraction • Meta-Analysis • Extracted ERP pattern labeling • Extracted ERP pattern clustering • Protocol incorporates and integrates: • ERP pattern extraction • ERP metric extraction/RDF generation • NEMO Data Base (NEMO Portal / NEMO FTP Server) • NEMO Knowledge Base (NEMO Ontology/Query Engine)

  5. ERP Pattern Segmentation ToolMATLAB and Directory Configuration • Get Latest Toolkit Version (NEMO Wiki : Screencasts : Versions) • Update your local (working) copy of the NEMO Sourceforge Repository • Configure MATLAB (NEMO Wiki : Screencasts : NEMO ERP Analysis Toolkit I) • MATLAB R2010a / R2010b, Optimization and Statistics Toolboxes • Add to the MATLAB path, with subfolders: • NEMO_ERP_Dataset_Import / NEMO_ERP_Dataset_Information • NEMO_ERP_Metric_Extraction / NEMO_ERP_Pattern_Decomposition / NEMO_ERP_Pattern_Segmentation • Configure Experiment Folder (NEMO Wiki : Screencasts : NEMO ERP Analysis Toolkit I & II) • Create an experiment-specific parent folder containing Data, Metric Extraction, Pattern Decompositionand Pattern Segmentationsubfolders • Copy the metric extraction, decomposition and segmentationscript templates from your NEMO Sourceforge Repository working copy to their respective script subfolders • Add the experiment-specific parent folder, with its subfolders, to the MATLAB path

  6. ERP Pattern Segmentation ToolMetascript Configuration – Step 1 of 6: Data Parameters • File_Name • Electrode_Montage_ID • Cell_Index • Factor_Index • ERP_Onset_Latency • ERP_Offset_Latency • ERP_Baseline_Latency

  7. ERP Pattern Segmentation ToolMetascript Configuration – Step 1 of 6: Data Parameters • File_Name • Name of an EGI segmented simple binary file, as a single-quoted string • Example: ‘SimErpData.raw’ • At present, Metric Extraction only accepts factor files from the Pattern Decomposition tool • Electrode_Montage_ID • Name of an EGI/Biosemi electrode montage file, as a single-quoted string • Valid montage strings: ‘GSN-128’, ‘GSN-256’, ‘HCGSN-128’, ‘HCGSN-256’, ‘Biosemi-64+5exg’, ‘Biosemi-64-sansNZ_LPA_RPA’ • The NEMO ERP Analysis Toolkit will require EEGLAB channel location file (.ced) format for all proprietary, user-specified, montages • Cell_Index • Indices of cells / conditions to import, as a MATLAB vector • Indices correspond to the ordering of cells in the data file • See Metric_obj.Dataset.Metadata.SrcFileInfo.Cellcode for the ordered list of conditions • Factor_Index • Indices of PCA factors to import, as a MATLAB vector • Indices correspond to the ordering of factors in the data file

  8. ERP Pattern Segmentation ToolMetascript Configuration – Step 1 of 6: Data Parameters • ERP_Onset_Latency • Time, in milliseconds, of the first ERP sample point to import, as a MATLAB scalar • 0 ms = stimulus onset • Positive values specify post-stimulus time points, negative values pre-stimulus time points • All latencies must be in integer multiples of the sampling interval (for example, +’ve / -’ve multiples of 4 ms @ 250 Hz) • ERP_Offset_Latency • Time, in milliseconds, of the last ERP sample point to import, as a MATLAB scalar • 0 ms = stimulus onset • Positive values specify post-stimulus time points, and must be greater than the ERP_Onset_Latency • ERP_Offset_Latency must not exceed the final data sample point (for example, a 1000 ms ERP with a 200 ms baseline: maximum 800msERP_Offset_Latency) • ERP_Baseline_Latency • Time, in negative milliseconds, of the pre-stimulus ERP sample points to exclude from import, as a MATLAB scalar • ERP_Baseline_Latency = 0  no baseline • To import pre-stimulus sample points, specify ERP_Baseline_Latency < ERP_Onset_Latency < 0 • All latencies must be within the data range (for example, a 1000 ms ERP with a 200 ms baseline: ERP_Baseline_Latency = -200 ms, ERP_Onset_Latency = 0 ms and ERP_Offset_Latency = 800 ms imports the 800 mspost-stimulus interval, including stimulus onset)

  9. ERP Pattern Segmentation ToolMetascript Configuration – Step 2 of 6: Experiment Parameters (Required) • Lab_ID • Experiment_ID • Session_ID • Subject_Group_ID • Subject_ID • Experiment_Info

  10. ERP Pattern Segmentation ToolMetascript Configuration – Step 2 of 6: Experiment Parameters (Required) • Lab_ID • Laboratory identification label, as a single-quoted string • Example: ‘My Simulated Lab’ • Experiment_ID • Experiment identification label, as a single-quoted string • Example: ‘My Simulated Experiment’ • Session_ID • Session identification label, as a single-quoted string • Example: ‘My Simulated Session’ • Subject_Group_ID • Subject group identification label, as a single-quoted string • Example: ‘My Simulated Subject Group’ • Subject_ID • Subject identification label, as a single-quoted string • Example: ‘My Simulated Subject # 1’ • Experiment_Info • Experiment note, as a single-quoted string • Example: ‘tPCA with Infomax rotation’

  11. ERP Pattern Segmentation ToolMetascript Configuration – Step 3of 6: Experiment Parameters (Optional) • Event_Type_Label • Stimulus_Type_Label • Stimulus_Modality_Label • Cell_Label_Descriptor

  12. ERP Pattern Segmentation ToolMetascript Configuration – Step 3 of 6: Experiment Parameters (Optional) • Event_Type_Label • MATLAB cell array of cell/condition event type labels • One label per cell/condition, as a single-quoted string • Example: {‘SimEventType1’, ‘SimEventType2’, ‘SimEventType3’} • Stimulus_Type_Label • MATLAB cell array of cell/condition stimulus type labels • One label per cell/condition, as a single-quoted string • Example: {‘SimStimulusType1’, ‘SimStimulusType2’, ‘SimStimulusType3’} • Stimulus_Modality_Label • MATLAB cell array of cell/condition stimulus modality labels • One label per cell/condition, as a single-quoted string • Example: {‘SimStimulusModality1’, ‘SimStimulusModality2’, ‘SimStimulusModality3’} • Cell_Label_Descriptor • MATLAB cell array of cell/condition description labels • One label per cell/condition, as a single-quoted string • Optional Labels: E-prime assigned cell codes imported from input data file • Example: {‘SimConditionDescription1’, ‘SimConditionDescription2’, ‘SimConditionDescription3’}

  13. ERP Pattern Segmentation ToolMetascript Configuration – Step 4 of 6: Pattern Segmentation Parameters • Dimension_Flag • Averaging_Protocol • Microstate_Algorithm • Minimum_Microstate - _Duration • Maximum_Transition - _Duration

  14. ERP Pattern Segmentation ToolMetascript Configuration – Step 4 of 6: Pattern Segmentation Parameters • Dimension_Flag • Specifies dimensionality of the coordinate space containing the +’ve / -’ve potential centroids, as a MATLAB scalar • Potential centroids are the locations of the centers of scalp-recorded positvity / negativity • Dimension_Flag = 2: Potential centroids are locations in 2D scalp “flat-map” space • Dimension_Flag = 3: Potential centroids are locations in 3D “head-volume” space • Averaging_Protocol • Specifies averaging precedence w.r.t. microstate boundary probability curve extraction, as a single-quoted string • ‘ExtractThanAverage’: Extract subject-specific microstate boundary probability curves, then average across subjects within each cell • ‘AverageThanExtract’: Average ERPs across subjects within each cell, then extract grand average microstate boundary probability curve • Microstate_Algorithm • Specifies the microstate boundary probability computation algorithm, as a MATLAB function handle • @CentroidDissimilarity1D: Considers changes in a 1-parameter centroid location function • @CentroidDissimilarity2D: Considers changes in a 2-parameter centroid location function • @GlobalMapDissimilarity: Considers changes in successive topographic map correlations • @GlobalFieldPower: Considers locations of minimum global field power

  15. ERP Pattern Segmentation ToolMetascript Configuration – Step 4 of 6: Pattern Segmentation Parameters • Minimum_Microstate_Duration • Specifies the minimum allowable interval for a stable topography to be designated a microstate • Specify Minimum_Microstate_Durationin milliseconds, as a MATLAB scalar • Maximum_Transition_Duration • Specifies the maximum allowable interval of unstable topography to be excluded from the beginning or end of a microstate region • Specify Maximum_Transition_Duration, in milliseconds, as a MATLAB scalar

  16. ERP Pattern Segmentation ToolMetascript Configuration – Step 5 of 6: Class Instantiation I Instantiate EGI reader class object Initialize object parameters Import metadata Import signal (ERP) data

  17. ERP Pattern Segmentation ToolMetascript Configuration – Step 5 of 6: Class Instantiation II Instantiate Pattern Segmentation class object Initialize object parameters

  18. ERP Pattern Segmentation ToolMetascript Configuration – Step 6of 6: Class Invocation for Grand Average Data Call ComputeMicrostateBoundaries method: Computes microstate boundaries via specified microstate algorithm Call ComputeMicrostateStatistics method: Exclude invalid microstates and compute microstate statistics Call PlotMicrostateAnalysis method: Plot microstate boundary probability curve, microstate statistics and microstate topographies

  19. ERP Pattern Segmentation ToolMetascript Configuration – Step 6of 6: Class Invocation for Subject Average Data Call ComputeMicrostateBoundaries method: Computes microstate boundaries via specified microstate algorithm Call ComputeMicrostateStatistics method: Exclude invalid microstates and compute microstate statistics Call PlotMicrostateAnalysis method: Plot microstate boundary probability curve, microstate statistics and microstate topographies

  20. ERP Pattern Segmentation ToolMetascript Configuration – Step 6of 6: Class Invocation for Subject-Specific Data Call ComputeMicrostateBoundaries method: Computes microstate boundaries via specified microstate algorithm Call ComputeMicrostateStatistics method: Exclude invalid microstates and compute microstate statistics Call PlotMicrostateAnalysis method: Plot microstate boundary probability curve, microstate statistics and microstate topographies `

  21. ERP Pattern Segmentation ToolPlot Microstate Analysis GUI – 40 millisecond Minimum_Microstate_Duration

  22. ERP Pattern Segmentation ToolPlot Microstate Analysis GUI – 30 millisecond Minimum_Microstate_Duration

  23. ERP Pattern Segmentation ToolFolder Output for SimErpData.raw • Pattern Segmentation output folder contents • NemoErpPatternSegmentation workspace object in MATLAB (.mat) format • That’s it for now Input data file Time stamp

  24. ERP Pattern Segmentation ToolViewing Pattern Segmentation Class Properties in MATLAB NemoErpPatternSegmentationobject EgiRawIO object • MATLAB Workspace view Double click to open…

  25. ERP Pattern Segmentation ToolViewing Pattern Segmentation Class Properties in MATLAB • MATLAB Workspace view • EPreadDataInput: MATLAB structure of input parameters to ep_readData • Epdata: MATLAB structure of output data and metadata from ep_readData • EGIreadDataInput: MATLAB structure of (optional) input parameters to EGI_readData and EGI_readMetaData • Metadata: MATLAB structure of output metadata from EGI_readMetadata • Data: MATLAB structure of output data from EGI_readData Keep on double clicking …

  26. ERP Pattern Segmentation ToolViewing Pattern Segmentation Class Properties in MATLAB • MATLAB Workspace view Keep on double clicking …

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