NEMO ERP Analysis Toolkit ERP Pattern Decomposition An Overview

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

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

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)

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

 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

 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

 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 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

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

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’

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

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’}

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

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’

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

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

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

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

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

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

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

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

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

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

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

ERP Pattern Decomposition Tool Plot Factor Variance GUI

 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

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

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

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