Transcript Presentation 20110211 4_Nemo_Segmentation_Overview_RF

NEMO ERP Analysis Toolkit
ERP Pattern Segmentation
An Overview
NEMO Information Processing Pipeline
NEMO Information Processing Pipeline
Pattern Decomposition Component
NEMO Information Processing Pipeline
ERP 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 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
ERP Pattern Segmentation Tool
Metascript Configuration – Step 1 of 6: Data Parameters
 File_Name
 Electrode_Montage_ID
 Cell_Index
 Factor_Index
 ERP_Onset_Latency
 ERP_Offset_Latency
 ERP_Baseline_Latency
ERP Pattern Segmentation Tool
Metascript 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 Pattern Segmentation Tool
Metascript Configuration – Step 1 of 6: 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 Segmentation Tool
Metascript Configuration – Step 2 of 6: Experiment Parameters (Required)
 Lab_ID
 Experiment_ID
 Session_ID
 Subject_Group_ID
 Subject_ID
 Experiment_Info
ERP Pattern Segmentation Tool
Metascript 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 Tool
Metascript Configuration – Step 3 of 6: Experiment Parameters (Optional)
 Event_Type_Label
 Stimulus_Type_Label
 Stimulus_Modality_Label
 Cell_Label_Descriptor
ERP Pattern Segmentation Tool
Metascript 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
 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 Segmentation Tool
Metascript Configuration – Step 4 of 6: Pattern Segmentation Parameters
 Dimension_Flag
 Averaging_Protocol
 Microstate_Algorithm
 Minimum_Microstate _Duration
 Maximum_Transition _Duration
ERP Pattern Segmentation Tool
Metascript Configuration – Step 4 of 6: Pattern Segmentation Parameters
 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 singlequoted 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
ERP Pattern Segmentation Tool
Metascript Configuration – Step 4 of 6: Pattern Segmentation Parameters
 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 Tool
Metascript 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 Tool
Metascript Configuration – Step 5 of 6: Class Instantiation II
Instantiate Pattern
Segmentation class object
Initialize object parameters
ERP Pattern Segmentation Tool
Metascript Configuration – Step 6 of 6: Class Invocation for Grand Average Data
Call
ComputeMicrostateBoundaries
method: Computes microstate
boundaries 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 Tool
Metascript Configuration – Step 6 of 6: Class Invocation for Subject Average Data
Call
ComputeMicrostateBoundaries
method: Computes microstate
boundaries 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 Tool
Metascript Configuration – Step 6 of 6: Class Invocation for Subject-Specific Data
Call
ComputeMicrostateBoundaries
method: Computes microstate
boundaries 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 Tool
Plot Microstate Analysis GUI – 40 millisecond Minimum_Microstate_Duration
ERP Pattern Segmentation Tool
Plot Microstate Analysis GUI – 30 millisecond Minimum_Microstate_Duration
ERP Pattern Segmentation Tool
Folder Output for SimErpData.raw
Pattern Segmentation output folder contents
– NemoErpPatternSegmentation workspace object
in MATLAB (.mat) format
– That’s it for now
Input data file
Time stamp
ERP Pattern Segmentation Tool
Viewing Pattern Segmentation Class Properties in MATLAB
NemoErpPatternSegmentation object
 MATLAB Workspace view
EgiRawIO object
Double click
to open…
ERP Pattern Segmentation Tool
Viewing Pattern Segmentation Class Properties in MATLAB
 MATLAB Workspace view
Keep on double
clicking …
 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
ERP Pattern Segmentation Tool
Viewing Pattern Segmentation Class Properties in MATLAB
 MATLAB Workspace view
Keep on double
clicking …