1 / 20

NEMO ERP Analysis Toolkit ERP Metric Extraction

NEMO ERP Analysis Toolkit ERP Metric Extraction. An Overview. NEMO Information Processing Pipelin e. NEMO Information Processing Pipelin e Metric Extraction Component. NEMO Information Processing Pipeline ERP Pattern Extraction, Identification and Labeling.

gurit
Download Presentation

NEMO ERP Analysis Toolkit ERP Metric Extraction

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. NEMO ERP Analysis ToolkitERP Metric Extraction An Overview

  2. NEMO Information Processing Pipeline

  3. NEMO Information Processing PipelineMetric Extraction Component

  4. 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 theirspatial, 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)

  5. ERP Metric Extraction 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 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

  6. ERP Metric Extraction ToolMetascript 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

  7. ERP Metric Extraction 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_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

  8. ERP Metric Extraction ToolMetascript 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 800msERP_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 mspost-stimulus interval, including stimulus onset)

  9. ERP Metric Extraction ToolMetascript Configuration – Step 2 of 6: Experiment Parameters (Required) • Lab_ID • Experiment_ID • Session_ID • Subject_Group_ID • Subject_ID • Experiment_Info

  10. ERP Metric Extraction 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’

  11. ERP Metric Extraction ToolMetascript Configuration – Step 3of 6: Experiment Parameters (Optional) • Event_Type_Label • Stimulus_Type_Label • Stimulus_Modality_Label • Cell_Label_Descriptor

  12. ERP Metric Extraction 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 • 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’}

  13. ERP Metric Extraction ToolMetascript 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’

  14. ERP Metric Extraction ToolMetascript 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

  15. ERP Metric Extraction ToolMetascript Configuration – Step 6of 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

  16. ERP Metric Extraction ToolFolder 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

  17. ERP Metric Extraction ToolExample 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) …

  18. ERP Metric Extraction ToolExample 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

  19. ERP Metric Extraction ToolViewing Metric Extraction Class Properties in MATLAB NemoErpMetricExtraction object EgiRawIO object • MATLAB Workspace view Double click to open…

  20. ERP Metric Extraction ToolViewing Metric Extraction Class Properties in MATLAB • MATLAB Workspace view Keep on double clicking …

More Related