NEMO ERP Analysis Toolkit ERP Pattern Decomposition

1. NEMO ERP Analysis ToolkitERP Pattern Decomposition An Overview

2. NEMO processing pipeline NEMO NIH Annual All-Hands Meeting

3. NEMO Data Analysis NEMO NIH Annual All-Hands Meeting

4. NEMO Information Processing PipelineERP Pattern Extraction, Identification and Labeling • Obtain ERP data sets with compatible functional constraints • NEMO consortium data • Decompose/ segmentERP 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 Decomposition 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 Segmentation subfolders • Copy the metric extraction, decompositionand 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. ERP Pattern Decomposition ToolMetascript Configuration – Step 1 of 7: Data Parameters • File_Name • Electrode_Montage_ID • Cell_Index • Factor_Index • ERP_Onset_Latency • ERP_Offset_Latency • ERP_Baseline_Latency

7. ERP Pattern Decomposition ToolMetascript Configuration – Step 1 of 7: 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 Decomposition ToolMetascript Configuration – Step 1 of 7: 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 Decomposition ToolMetascript 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 ToolMetascript 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 ToolMetascript Configuration – Step 3of 7: Experiment Parameters (Optional) • Event_Type_Label • Stimulus_Type_Label • Stimulus_Modality_Label • Cell_Label_Descriptor

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

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

17. ERP Pattern Decomposition ToolMetascript 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 ToolMetascript 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 ToolMetascript Configuration – Step 5of 7: Export to EGI Simple Binary Parameters Stage 1 • Num_Fac_Export • Num_Fac_Export_st • Cell_IO_Rule • Output_File_Type • Grand_Avg_Add • Exclude_Channel Stage 2

20. ERP Pattern Decomposition ToolMetascript Configuration – Step 5of 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 <MyPatternDecompositionObject>.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 ToolMetascript Configuration – Step 6of 7: Class Instantiation I Instantiate EGI reader class object Initialize object parameters Import metadata Import signal (ERP) data

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

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

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

25. ERP Pattern Decomposition ToolMetascript Configuration – Step 7 of 7: Class Invocation (EP Toolkit) Call ComputeTwoStagePCA method: Two stage PCA decomposition Call OneStagePCAtoEgiSbinmethod: Export One stage PCA decomposition results Call TwoStagePCAtoEgiSbinmethod: 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 ToolPlot Factor Variance GUI

27. ERP Pattern Decomposition ToolFolder Output for SimErpData.raw • 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 Input data file Time stamp

28. ERP Pattern Decomposition ToolViewing Pattern Decomposition Class Properties in MATLAB NemoErpPatternDecompositionobject EgiRawIO object • MATLAB Workspace view Double click to open…

29. ERP Pattern Decomposition ToolViewing Pattern Decomposition 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 …

30. ERP Pattern Decomposition ToolViewing Pattern Decomposition Class Properties in MATLAB • MATLAB Workspace view • 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 …