nemo erp analysis toolkit erp pattern segmentation an overview

NEMO ERP Analysis ToolkitERP Pattern Segmentation

An Overview

NEMO Information Processing Pipeline

NEMO Information Processing PipelinePattern Decomposition Component

NEMO Information Processing PipelineERP Pattern Segmentation, 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 Segmentation 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 Segmentation ToolMetascript Configuration – Step 1 of 6: 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 Segmentation ToolMetascript Configuration – Step 2 of 6: Experiment Parameters (Required)

Lab_ID

Experiment_ID

Session_ID

Subject_Group_ID

Subject_ID

Experiment_Info

ERP Pattern Segmentation ToolMetascript Configuration – Step 2 of 6: 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 Segmentation ToolMetascript Configuration – Step 3 of 6: Experiment Parameters (Optional)

Event_Type_Label

Stimulus_Type_Label

Stimulus_Modality_Label

Cell_Label_Descriptor

ERP Pattern Segmentation ToolMetascript Configuration – Step 3 of 6: 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


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 Segmentation ToolMetascript Configuration – Step 4 of 6: Pattern Segmentation Parameters

Dimension_Flag

Averaging_Protocol

Microstate_Algorithm

Minimum_Microstate - _Duration

Maximum_Transition - _Duration


Dimension_Flag– Specifies dimensionality of the coordinate space containing the +’ve / -’ve potential centroids, as a MATLAB

scalar Potential centroids are the locations of the centers of scalp-recorded positvity / negativity

Dimension_Flag = 2: Potential centroids are locations in 2D scalp “flat-map” space

Dimension_Flag = 3: Potential centroids are locations in 3D “head-volume” space

Averaging_Protocol– Specifies averaging precedence w.r.t. microstate boundary probability curve extraction, as a single-quoted

string

‘ExtractThanAverage’: Extract subject-specific microstate boundary probability curves, then average across

subjects within each cell

‘AverageThanExtract’: Average ERPs across subjects within each cell, then extract grand average microstate

boundary probability curve

Microstate_Algorithm– Specifies the microstate boundary probability computation algorithm, as a MATLAB function handle

@CentroidDissimilarity1D: Considers changes in a 1-parameter centroid location function

@CentroidDissimilarity2D: Considers changes in a 2-parameter centroid location function

@GlobalMapDissimilarity: Considers changes in successive topographic map correlations

@GlobalFieldPower: Considers locations of minimum global field power


Minimum_Microstate_Duration

– Specifies the minimum allowable interval for a stable topography to be designated a

microstate

– Specify Minimum_Microstate_Duration in milliseconds, as a MATLAB scalar

Maximum_Transition_Duration

– Specifies the maximum allowable interval of unstable topography to be excluded

from the beginning or end of a microstate region

– Specify Maximum_Transition_Duration, in milliseconds, as a MATLAB scalar

ERP Pattern Segmentation ToolMetascript Configuration – Step 5 of 6: Class Instantiation I

Instantiate EGI reader class object

Initialize object parameters

Import metadata

Import signal (ERP) data

ERP Pattern Segmentation ToolMetascript Configuration – Step 5 of 6: Class Instantiation II

Instantiate Pattern Segmentation class object

Initialize object parameters

ERP Pattern Segmentation ToolMetascript Configuration – Step 6 of 6: Class Invocation for Grand Average Data

Call ComputeMicrostateBoundaries method: Computes microstateboundaries via specified microstate algorithm

Call ComputeMicrostateStatistics method: Exclude invalid microstates and compute microstate statistics

Call PlotMicrostateAnalysis method: Plot microstate boundary probability curve, microstate statistics and microstate topographies

ERP Pattern Segmentation ToolMetascript Configuration – Step 6 of 6: Class Invocation for Subject Average Data




ERP Pattern Segmentation ToolMetascript Configuration – Step 6 of 6: Class Invocation for Subject-Specific Data




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ERP Pattern Segmentation ToolPlot Microstate Analysis GUI – 40 millisecond Minimum_Microstate_Duration

ERP Pattern Segmentation ToolPlot Microstate Analysis GUI – 30 millisecond Minimum_Microstate_Duration

Pattern Segmentation output folder contents– NemoErpPatternSegmentation workspace object

in MATLAB (.mat) format

– That’s it for now

ERP Pattern Segmentation ToolFolder Output for SimErpData.raw

Input data file Time stamp

ERP Pattern Segmentation ToolViewing Pattern Segmentation Class Properties in MATLAB

MATLAB Workspace view

NemoErpPatternSegmentation 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

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nemo erp analysis toolkit erp pattern segmentation an overview

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