1. NEMO ERP Analysis Toolkit ERP Pattern Segmentation An Overview

2. NEMO Information Processing Pipeline

3. NEMO Information Processing Pipeline Pattern Decomposition Component

4. NEMO Information Processing Pipeline ERP 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 their spatial, 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 Tool MATLAB 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 Decomposition and Pattern Segmentation subfolders – Copy the metric extraction, decomposition and segmentation script 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.  File_Name  Electrode_Montage_ID  Cell_Index  Factor_Index  ERP_Onset_Latency  ERP_Offset_Latency  ERP_Baseline_Latency ERP Pattern Segmentation Tool Metascript Configuration – Step 1 of 6: Data Parameters

7.  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 ERP Pattern Segmentation Tool Metascript Configuration – Step 1 of 6: Data Parameters

8.  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 800 ms ERP_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 ms post-stimulus interval, including stimulus onset) ERP Pattern Segmentation Tool Metascript Configuration – Step 1 of 6: Data Parameters

9. ERP Pattern Segmentation Tool Metascript 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 Tool Metascript 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 Tool Metascript Configuration – Step 3 of 6: Experiment Parameters (Optional)  Event_Type_Label  Stimulus_Type_Label  Stimulus_Modality_Label  Cell_Label_Descriptor

12. ERP Pattern Segmentation Tool Metascript 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 Tool Metascript Configuration – Step 4 of 6: Pattern Segmentation Parameters  Dimension_Flag  Averaging_Protocol  Microstate_Algorithm  Minimum_Microstate - _Duration  Maximum_Transition - _Duration

14. ERP Pattern Segmentation Tool Metascript 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 Tool Metascript 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_Duration in 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 Tool Metascript 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 Tool Metascript Configuration – Step 5 of 6: Class Instantiation II Instantiate Pattern Segmentation class object Initialize object parameters

18. ERP Pattern Segmentation Tool Metascript Configuration – Step 6 of 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 Tool Metascript Configuration – Step 6 of 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 Tool Metascript Configuration – Step 6 of 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 Tool Plot Microstate Analysis GUI – 40 millisecond Minimum_Microstate_Duration

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

23.  Pattern Segmentation output folder contents – NemoErpPatternSegmentation workspace object in MATLAB (.mat) format – That’s it for now ERP Pattern Segmentation Tool Folder Output for SimErpData.raw Input data fileTime stamp

24. ERP Pattern Segmentation Tool Viewing Pattern Segmentation Class Properties in MATLAB  MATLAB Workspace view NemoErpPatternSegmentation object EgiRawIO object Double click to open…

25. ERP Pattern Segmentation Tool Viewing Pattern Segmentation Class Properties in MATLAB  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 …  MATLAB Workspace view

26. ERP Pattern Segmentation Tool Viewing Pattern Segmentation Class Properties in MATLAB  MATLAB Workspace view Keep on double clicking …