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Ontology-Based Mining of Brainwaves: A Sequence Similarity Technique for Mapping Alternative Features in Event-Related Potentials (ERP) Data

  • Haishan Liu
  • Gwen Frishkoff
  • Robert Frank
  • Dejing Dou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6119)

Abstract

In this paper, we present a method for identifying correspondences, or mappings, between alternative features of brainwave activity in event-related potentials (ERP) data. The goal is to simulate mapping across results from heterogeneous methods that might be used in different neuroscience research labs. The input to the mapping consists of two ERP datasets whose spatiotemporal characteristics are captured by alternative sets of features, that is, summary spatial and temporal measures capturing distinct neural patterns that are linked to concepts in a set of ERP ontologies, called NEMO (Neural ElectroMagnetic Ontologies) [3, 6]. The feature value vector of each summary metric is transformed into a point-sequence curve, and clustering is performed to extract similar subsequences (clusters) representing the neural patterns that can then be aligned across datasets. Finally, the similarity between measures is derived by calculating the similarity between corresponding point-sequence curves. Experiment results showed that the proposed approach is robust and has achieved significant improvement on precision than previous algorithms.

Keywords

Schema Matching Sequence Similarity Search ERP Data 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Haishan Liu
    • 1
  • Gwen Frishkoff
    • 2
    • 3
  • Robert Frank
    • 2
  • Dejing Dou
    • 1
  1. 1.Computer and Information Science DepartmentUniversity of OregonEugene
  2. 2.NeuroInformatics CenterUniversity of OregonEugene
  3. 3.Department of Psychology & Neuroscience InstituteGeorgia State UniversityAtlanta

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