nemo erp analysis toolkit erp pattern decomposition an overview
Post on 19-Dec-2015
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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 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 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 1 of 7: Data Parameters
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
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 ToolMetascript 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 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’
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
ERP Pattern Decomposition ToolMetascript 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 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 sPCAStage 1 sPCA Stage 2 tPCA
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
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
Initialize object parameters
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
Initialize object parameters
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
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…
ERP Pattern Decomposition ToolViewing 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 ToolViewing 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