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Report
NEMO ERP Analysis Toolkit
ERP Metric Extraction
An Overview
NEMO Information Processing Pipeline
NEMO Information Processing Pipeline
Metric Extraction Component
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 Metric Extraction 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 script 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 Metric Extraction 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 Metric Extraction 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_tPCA_GAV.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 Metric Extraction 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 Metric Extraction Tool
Metascript Configuration – Step 2 of 6: Experiment Parameters (Required)
 Lab_ID
 Experiment_ID
 Session_ID
 Subject_Group_ID
 Subject_ID
 Experiment_Info
ERP Metric Extraction 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 Metric Extraction Tool
Metascript Configuration – Step 3 of 6: Experiment Parameters (Optional)
 Event_Type_Label
 Stimulus_Type_Label
 Stimulus_Modality_Label
 Cell_Label_Descriptor
ERP Metric Extraction 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 Metric Extraction Tool
Metascript Configuration – Step 4 of 6: NemoErpMetricExtraction Parameters
 ERP_Component_Label
 ERP_Component_Analysis_
Method_Label
 ERP_Component_Label
–
ERP individual component identification label, as a single-quoted string
 Example: ‘PcaFactor#’ or ‘MicrostateSegment#’
 ERP_Component_Analysis_Method_Label
–
ERP component-generation-procedure identification label, as a single-quoted string
 Example: ‘tPCA with Infomax rotation’ or ‘Microstate segmentation via Centroid Dissimilarity’
ERP Metric Extraction Tool
Metascript Configuration – Step 5 of 6: Class Instantiation
Instantiate EGI reader class
object
Initialize object parameters
Import metadata
Import signal (ERP) data
Instantiate Metric Extraction
class object
Initialize object parameters
ERP Metric Extraction Tool
Metascript Configuration – Step 6 of 6: Class Invocation
Call RDF method: Generate
RDF-formatted metric info
Call CSV method: Generate
CSV-formatted metric info
Call XLS method: Generate
XLS-formatted metric info
ERP Metric Extraction Tool
Folder Output for SimErpData_tPCA_GAV.raw
 Metric Extraction output folder contents
– CSV files, one per condition
– RDF files, one per condition
– NemoErpMetricExraction object in MATLAB (.mat) format
Input data file
Time stamp
ERP Metric Extraction Tool
Example Output for SimErpData_tPCA_GAV.raw
 Comma Separated Value (CSV) format output file
– Column 1: Factor Label
– Column 2: Metric Label
– Column 3: Metric Value (microvolts | milliseconds)
PCAfactor#001
PCAfactor#002
PCAfactor#003
PCAfactor#004
PCAfactor#005
PCAfactor#006
PCAfactor#001
PCAfactor#002
PCAfactor#003
PCAfactor#004
PCAfactor#005
PCAfactor#006
PCAfactor#001
PCAfactor#002
PCAfactor#003
PCAfactor#004
PCAfactor#005
PCAfactor#006
PCAfactor#001
PCAfactor#002
PCAfactor#003
PCAfactor#004
PCAfactor#005
PCAfactor#006
Mean_Intensity_LATEMP
Mean_Intensity_LATEMP
Mean_Intensity_LATEMP
Mean_Intensity_LATEMP
Mean_Intensity_LATEMP
Mean_Intensity_LATEMP
Mean_Intensity_LFRONT
Mean_Intensity_LFRONT
Mean_Intensity_LFRONT
Mean_Intensity_LFRONT
Mean_Intensity_LFRONT
Mean_Intensity_LFRONT
Mean_Intensity_LOCC
Mean_Intensity_LOCC
Mean_Intensity_LOCC
Mean_Intensity_LOCC
Mean_Intensity_LOCC
Mean_Intensity_LOCC
Mean_Intensity_LORB
Mean_Intensity_LORB
Mean_Intensity_LORB
Mean_Intensity_LORB
Mean_Intensity_LORB
Mean_Intensity_LORB
-1.32205108
2.20884825
-0.13632037
0.32797573
-0.80275749
0.04743715
-0.65896539
-1.63287792
-1.73317912
-2.09422301
-1.42150766
-0.03723651
0.28687667
-3.38753124
1.40419426
0.61821343
3.22377541
-0.28709995
-1.91178549
3.14364142
-0.85076154
0.35483038
-1.20096476
0.07527227
…
PCAfactor#001
PCAfactor#002
PCAfactor#003
PCAfactor#004
PCAfactor#005
PCAfactor#006
PCAfactor#001
PCAfactor#002
PCAfactor#003
PCAfactor#004
PCAfactor#005
PCAfactor#006
PCAfactor#001
PCAfactor#002
PCAfactor#003
PCAfactor#004
PCAfactor#005
PCAfactor#006
PCAfactor#001
PCAfactor#002
PCAfactor#003
PCAfactor#004
PCAfactor#005
PCAfactor#006
Mean_Intensity_RORB
Mean_Intensity_RORB
Mean_Intensity_RORB
Mean_Intensity_RORB
Mean_Intensity_RORB
Mean_Intensity_RORB
Mean_Intensity_RPAR
Mean_Intensity_RPAR
Mean_Intensity_RPAR
Mean_Intensity_RPAR
Mean_Intensity_RPAR
Mean_Intensity_RPAR
Mean_Intensity_RPTEMP
Mean_Intensity_RPTEMP
Mean_Intensity_RPTEMP
Mean_Intensity_RPTEMP
Mean_Intensity_RPTEMP
Mean_Intensity_RPTEMP
Peak_Latency_Measurement_Datum
Peak_Latency_Measurement_Datum
Peak_Latency_Measurement_Datum
Peak_Latency_Measurement_Datum
Peak_Latency_Measurement_Datum
Peak_Latency_Measurement_Datum
-1.92933138
3.13058562
-0.63761322
0.36211667
-1.21687127
0.07518651
2.80682594
-1.13138179
-0.0710425
-0.35024415
-0.38600676
0.15280392
-0.3813105
0.12308511
0.92244155
0.72990109
0.92419572
-0.0698178
484
216
260
252
116
204
ERP Metric Extraction Tool
Example Output for SimErpData_tPCA_GAV.raw
 Resource Description Format (RDF) format output file
– RDF N-Triple syntax
– Subject, Predicate (Relation), Object triple
– Example: Subject, has property, object property
ERP Metric Extraction Tool
Viewing Metric Extraction Class Properties in MATLAB
 MATLAB Workspace view
NemoErpMetricExtraction object
EgiRawIO object
Double click
to open…
ERP Metric Extraction Tool
Viewing Metric Extraction Class Properties in MATLAB
 MATLAB Workspace view
Keep on double
clicking …

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