Haishan Liu 1, Gwen Frishkoff 2, Robert Frank 1, Dejing Dou 1 1 University of Oregon 2 Georgia State University

 ERP (Event-Related Potentials): a direct measure of neural activity  Lack of meta-analysis across experiment  NEMO (Neural ElectroMagnetic Ontologies) for data sharing and integration  Goal of the presented study  Mapping alternative sets of ERP spatial and temporal measures

Alternative sets of ERP metrics

 Semi-structured data

 Uninformative column headers

 Semi-structured data  Uninformative column headers  Numerical values

 Value vector to point-sequence curve  Use clustering analysis to identify subsequences  Assign meaningful labels to subsequences  Align subsequences across different datasets  Sequence post-processing  Evaluate similarities of the curves to determine the mapping (cross-spatial join)

Cluster labels Meaningful labels Point-sequence curve

 Process all point- sequence curves  Calculate Euclidean distance between sequences in the Cartesian product set (Cross-spatial join) ● ● ●● ● ● Metric Set1 Metric Set2

 the two datasets contain the same or similar ERP patterns

 1-to-1 mapping between metrics

 the two datasets contain the same or similar ERP patterns  1-to-1 mapping between metrics  Minimum sum of distances >

Wrong Mappings. Precision = 9/13 Gold standard mapping falls along the diagonal cells

 3-Factor design of experiment data (Fully factorial: 2 x 2 x 2)  2 simulated “subject groups” (samples) ▪ SG1 = sample 1 ▪ SG2 = sample 2  2 data decompositions ▪ tPCA = temporal PCA decomposition ▪ sICA = spatial ICA decomposition  2 sets of alternative metrics ▪ m1 = metric set 1 ▪ m2 = metric set 2

Overall Precision: 84.6%

 We describe a method for identifying mappings between alternative sets of ERP measures.  Use of an ontology to assign meaningful labels to ERP patterns  Application of sequence similarity search in discovering mappings across alternative metrics  Extension of the instance-level approach in schema matching  Articulation of a global minimum heuristic

 Assuming datasets to contain the dissimilar ERP patterns  From 1-to-1 mapping to complex mapping  Approximation of the global minimum heuristics  More tests on real-world data

Questions and comments? Please contact Haishan Liu