1. NEMO ERP Analysis Toolkit ERP Pattern Decomposition An Overview

2. NEMO processing pipeline 2/11/11NEMO NIH Annual All-Hands Meeting2

3. NEMO Data Analysis 2/11/11NEMO NIH Annual All-Hands Meeting3

4. NEMO Information Processing Pipeline ERP Pattern Extraction, 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 Decomposition 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 Decomposition Tool Metascript Configuration – Step 1 of 7: 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 Decomposition Tool Metascript Configuration – Step 1 of 7: 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 Decomposition Tool Metascript Configuration – Step 1 of 7: Data Parameters

9. ERP Pattern Decomposition Tool Metascript Configuration – Step 2 of 7: Experiment Parameters (Required)  Lab_ID  Experiment_ID  Session_ID  Subject_Group_ID  Subject_ID  Experiment_Info

10. ERP Pattern Decomposition Tool Metascript Configuration – Step 2 of 7: 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 Decomposition Tool Metascript Configuration – Step 3 of 7: Experiment Parameters (Optional)  Event_Type_Label  Stimulus_Type_Label  Stimulus_Modality_Label  Cell_Label_Descriptor

12. ERP Pattern Decomposition Tool Metascript Configuration – Step 3 of 7: 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 Decomposition Tool Metascript Configuration – Step 4 of 7: Stage 1 Component Decomposition Parameters  PCAmode  MAT_TYPE  ROTATION  LOADING  NUM_FAC  SORTOPT  GAVE Stage 1 tPCA

14. ERP Pattern Decomposition Tool Metascript Configuration – Step 4 of 7: Stage 1 Component Decomposition Parameters  PCAmode – Specifies the PCA mode, as a single-quoted string  ‘temp’: Temporal PCA, in which time points are variables  ‘spat’: Spatial PCA, in which channel voltages are variables  MAT_TYPE – Specifies the PCA eigenvector/relationship matrix, as a single-quoted string  ‘COV’: Covariance matrix (mean correction)  ‘COR’: Correlation matrix (mean + variance correction)  ‘SCP’: Sum of squares cross product (no mean/variance correction)  ROTATION – Specifies the PCA factor rotation type, as a single-quoted string  ‘IMAX’: Infomax - ”Statistically Independent” factor loadings via high-order statistics  ‘VMAX’: Varimax - Maximal variance factor loadings subject to orthogonality constraint  ‘PMAX’: Promax - Relaxes factor orthogonality constraint of relationship matrix eigenvectors Promax rotation is automatically applied subsequent to Varimax rotation when ROTATION = ‘VMAX’

15. ERP Pattern Decomposition Tool Metascript Configuration – Step 4 of 7: Stage 1 Component Decomposition Parameters  LOADING – Specifies factor loading type, the rotated factor loading scaling transform, as a single-quoted string  ‘N’: None  ‘K’: Kaiser  ‘C’: Covariance  ‘W’: Cureton-Mulaik  NUM_FAC – Specifies the number of PCA factors to rotate, as a MATLAB scalar  For sPCA: 1.LE. NUM_FAC.LE. number of electrode channels  For tPCA: 1.LE. NUM_FAC.LE. number of imported ERP time points  SORTOPT – Specifies the ordering (sort) of post-rotation PCA factors, as a single quoted string  ‘PreRot’: Sort in order of decreasing pre-rotation (eigenvector) factor variance  ‘FacVar’: Sort in order of decreasing post-rotation factor variance, via FacVar parameter  GAVE – Optionally perform analysis on grand average data  ‘N’: Perform analysis on subject average data only  ‘Y’: Perform analysis on grand average data; convert factor scores to subject average form for export

16. ERP Pattern Decomposition Tool Metascript Configuration – Step 4 of 7: Stage 2 Component Decomposition Parameters  MAT_TYPE_st  ROTATION_st  LOADING_st  NUM_FAC_st  SORTOPT_st Stage 1 tPCA _st  spatio-temporal or stage 2 PCA parameters

17. ERP Pattern Decomposition Tool Metascript Configuration – Step 4 of 7: Stage 2 Component Decomposition Parameters  PCAmode – Specifies the PCA mode, as a single-quoted string  ‘temp’: Temporal PCA, in which time points are variables  ‘spat’: Spatial PCA, in which channel voltages are variables  MAT_TYPE_st – Specifies the PCA eigenvector/relationship matrix, as a single-quoted string  ‘COV’: Covariance matrix (mean correction)  ‘COR’: Correlation matrix (mean + variance correction)  ‘SCP’: Sum of squares cross product (no mean/variance correction)  ROTATION_st – Specifies the PCA factor rotation type, as a single-quoted string  ‘IMAX’: Infomax - ”Statistically Independent” factor loadings via high-order statistics  ‘VMAX’: Varimax - Maximal variance factor loadings subject to orthogonality constraint  ‘PMAX’: Promax - Relaxes factor orthogonality constraint of relationship matrix eigenvectors Promax rotation is automatically applied subsequent to Varimax rotation when ROTATION = ‘VMAX’ Stage 1 tPCA  Stage 2 sPCA Stage 1 sPCA  Stage 2 tPCA

18. ERP Pattern Decomposition Tool Metascript Configuration – Step 4 of 7: Stage 2 Component Decomposition Parameters  LOADING_st – Specifies factor loading type, the rotated factor loading scaling transform, as a single-quoted string  ‘N’: None  ‘K’: Kaiser  ‘C’: Covariance  ‘W’: Cureton-Mulaik  NUM_FAC_st – Specifies the number of PCA factors to rotate, as a MATLAB scalar  1.LE. NUM_FAC_st.LE. NUM_FAC (Number of stage 1 factors to rotate)  SORTOPT_st – Specifies the ordering (sort) of post-rotation PCA factors, as a single quoted string  ‘PreRot’: Sort in order of decreasing pre-rotation (eigenvector) factor variance  ‘FacVar’: Sort in order of decreasing post-rotation factor variance, via FacVar parameter  GAVE – Optionally perform analysis on grand average data  ‘N’: Perform analysis on subject average data only  ‘Y’: Perform analysis on grand average data; convert factor scores to subject average form for export Specified in Stage 1

19. ERP Pattern Decomposition Tool Metascript Configuration – Step 5 of 7: Export to EGI Simple Binary Parameters  Num_Fac_Export  Num_Fac_Export_st  Cell_IO_Rule  Output_File_Type  Grand_Avg_Add  Exclude_Channel Stage 1 Stage 2

20. ERP Pattern Decomposition Tool Metascript Configuration – Step 5 of 7: Export to EGI Simple Binary Parameters  Num_Fac_Export / Num_Fac_Export_st – Specifies the number of stage 1 / stage 2 PCA factors to export, as a MATLAB scalar  1.LE. Num_Fac_Export.LE. NUM_FAC (# of stage 1 PCA factors to rotate)  1.LE. Num_Fac_Export_st.LE. NUM_FAC_st (# of stage 2 PCA factors to rotate)  Cell_IO_Rule – Specifies the input cell to output cell rule, as a 2D MATLAB array  Output cell x input cell logical indexing matrix  Type.HelpTopic(‘PCAtoEgiSbin’) For Detail  Output_File_Type – Specifies the output PCA factor file type, as a single quoted string  ‘G’: Grand average factor file (Average across subject factors for each cell type | 1 file)  ‘S’: Subject average factor file (Subject-specific factors for each cell type | 1 file per subject)  Grand_Avg_Add – Specifies option to add grand average to factor reconstructions  ‘N’: Do not add grand average to factor reconstructions  ‘Y’: Add grand average to factor reconstructions  Exclude_Channel – List of peri-ocular or midline channels to omit in ANOVA (N/A = []), as a MATLAB vector

21. ERP Pattern Decomposition Tool Metascript Configuration – Step 6 of 7: Class Instantiation I Instantiate EGI reader class object Initialize object parameters Import metadata Import signal (ERP) data

22. ERP Pattern Decomposition Tool Metascript Configuration – Step 6 of 7: Class Instantiation I (EP Toolkit) Instantiate EGI reader class object Initialize object parameters Import metadata and signal (ERP) data via EPToolkit’s ep_readData

23. ERP Pattern Decomposition Tool Metascript Configuration – Step 6 of 7: Class Instantiation II Instantiate Pattern Decomposition class object Initialize object parameters

24. ERP Pattern Decomposition Tool Metascript Configuration – Step 7 of 7: Class Invocation Call ComputeTwoStagePCA method: Two stage PCA decomposition Call OneStagePCAtoEgiSbin method: Export One stage PCA decomposition results Call TwoStagePCAtoEgiSbin method: Export Two stage PCA decomposition results Call PlotFactorVariance method: Plot unrotated factor scree and rotated factor variance

25. ERP Pattern Decomposition Tool Metascript Configuration – Step 7 of 7: Class Invocation (EP Toolkit) Call ComputeTwoStagePCA method: Two stage PCA decomposition Call OneStagePCAtoEgiSbin method: Export One stage PCA decomposition results Call TwoStagePCAtoEgiSbin method: Export Two stage PCA decomposition results Call TwoStagePCAtoEPworkCache method: Exports EPworkCache folder Call PlotFactorVariance method: Plot unrotated factor scree and rotated factor variance

26. ERP Pattern Decomposition Tool Plot Factor Variance GUI

27.  Pattern Decomposition output folder contents – RAW files tPCA: InputDataFile_tPCA_GAV/AVG.raw sPCA: InputDataFile_sPCA_GAV/AVG.raw stPCA/tsPCA: InputDataFile_stPCA/tsPCA_GAV/AVG.raw – Epwork Folder: EP Toolkit integration folder (if used EPT_readData) – NemoErpPatternDecompostion workspace object in MATLAB (.mat) format ERP Pattern Decomposition Tool Folder Output for SimErpData.raw Input data fileTime stamp

28. ERP Pattern Decomposition Tool Viewing Pattern Decomposition Class Properties in MATLAB  MATLAB Workspace view NemoErpPatternDecomposition object EgiRawIO object Double click to open…

29. ERP Pattern Decomposition Tool Viewing Pattern Decomposition 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

30. ERP Pattern Decomposition Tool Viewing Pattern Decomposition Class Properties in MATLAB  EPdoPCAInput: MATLAB structure of input parameters to ep_doPCA  FactorResults: MATLAB structure of output factor decomposition and metadata from ep_doPCA  EPdoPCAstInput: MATLAB structure of input parameters to second PCA step (ep_doPCAst)  FactorResultsST: MATLAB structure of output factor decomposition and metadata from second PCA step (ep_doPCAst)  PCAtoEgiSbin: MATLAB structure of input parameters to OneStagePCAtoEgiSbin / TwoStagePCAtoEgiSbin Keep on double clicking …  MATLAB Workspace view