Download - NEMO ERP Analysis Toolkit ERP Pattern Decomposition

NEMO ERP Analysis ToolkitERP Pattern Decomposition

An Overview

NEMO NIH Annual All-Hands Meeting

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NEMO processing pipeline

2/11/11

NEMO NIH Annual All-Hands Meeting

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NEMO Data Analysis

2/11/11

NEMO Information Processing PipelineERP 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 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 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 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


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 ToolMetascript Configuration – Step 2 of 7: Experiment Parameters (Required)

Lab_ID

Experiment_ID

Session_ID

Subject_Group_ID

Subject_ID

Experiment_Info

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’

ERP Pattern Decomposition ToolMetascript Configuration – Step 3 of 7: Experiment Parameters (Optional)

Event_Type_Label

Stimulus_Type_Label

Stimulus_Modality_Label

Cell_Label_Descriptor

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


Example: {‘SimStimulusModality1’, ‘SimStimulusModality2’, ‘SimStimulusModality3’}

Cell_Label_Descriptor– MATLAB cell array of cell/condition description labels


Optional Labels: E-prime assigned cell codes imported from input data file

Example: {‘SimConditionDescription1’, ‘SimConditionDescription2’, ‘SimConditionDescription3’}

ERP Pattern Decomposition ToolMetascript Configuration – Step 4 of 7: Stage 1 Component Decomposition Parameters

PCAmode

MAT_TYPE

ROTATION

LOADING

NUM_FAC

SORTOPT

GAVE

Stage 1 tPCA


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’


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


MAT_TYPE_st

ROTATION_st

LOADING_st

NUM_FAC_st

SORTOPT_st

Stage 1 tPCA

_st spatio-temporal or stage 2 PCA 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 sPCAStage 1 sPCA Stage 2 tPCA


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 ToolMetascript 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 ToolMetascript 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 <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

ERP Pattern Decomposition ToolMetascript 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 ToolMetascript Configuration – Step 6 of 7: Class Instantiation I (EP Toolkit)

Instantiate EGI reader class object


Import metadata and signal (ERP) data via EPToolkit’s ep_readData

ERP Pattern Decomposition ToolMetascript Configuration – Step 6 of 7: Class Instantiation II

Instantiate Pattern Decomposition class object


ERP Pattern Decomposition ToolMetascript 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 ToolMetascript 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 ToolPlot 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 ToolFolder Output for SimErpData.raw

Input data file Time stamp

ERP Pattern Decomposition ToolViewing Pattern Decomposition Class Properties in MATLAB

MATLAB Workspace view NemoErpPatternDecomposition object

EgiRawIO object

Double click to open…


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


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