Ontology-Based Mining of Brainwaves: A Sequence Similarity Technique for Mapping Alternative Features in Event-Related Potentials (ERP) Data
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.
KeywordsSchema Matching Sequence Similarity Search ERP Data
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- 1.Agrawal, R., Faloutsos, C., Swami, A.N.: Efficient similarity search in sequence databases. In: Lomet, D.B. (ed.) FODO 1993. LNCS, vol. 730, pp. 69–84. Springer, Heidelberg (1993)Google Scholar
- 3.Dou, D., Frishkoff, G., Rong, J., Frank, R., Malony, A., Tucker, D.: Development of NeuroElectroMagnetic Ontologies (NEMO): A Framework for Mining Brain Wave Ontologies. In: Proceedings of the 13th ACM International Conference on Knowledge Discovery and Data Mining (KDD 2007), pp. 270–279 (2007)Google Scholar
- 5.Frishkoff, G.A., Frank, R.M., Rong, J., Dou, D., Dien, J., Halderman, L.K.: A Framework to Support Automated Classification and Labeling of Brain Electromagnetic Patterns. Computational Intelligence and Neuroscience (CIN), Special Issue, EEG/MEG Analysis and Signal Processing 2007 13 (2007)Google Scholar
- 6.Frishkoff, G., Le Pendu, P., Frank, R., Liuand, H., Dou, D.: Development of Neural Electromagnetic Ontologies (NEMO): Ontology-based Tools for Representation and Integration of Event-related Brain Potentials. In: Proceedings of the International Conference on Biomedical Ontology, ICBO 2009 (2009)Google Scholar
- 8.Wache, H., Vogele, T., Visser, U., Stuckenschmidt, H., Schuster, G., Neumann, H., Hubner, S.: Ontology-based integration of information: A survey of existing approaches. In: IJCAI-01 Workshop: Ontologies and Information Sharing, pp. 108–117 (2001)Google Scholar
- 9.Dhamankar, R., Lee, Y., Doan, A., Halevy, A.Y., Domingos, P.: iMAP: Discovering Complex Mappings between Database Schemas. In: Proceedings of the ACM Conference on Management of Data, pp. 383–394 (2004)Google Scholar
- 11.Sheth, A., Larson, J., Cornelio, A., Navathe, S.: A tool for integrating conceptual schemas and user views. In: Proc. 4th International Conference on Data Engineering (ICDE), Los Angeles, CA, US, pp. 176–183 (1988)Google Scholar
- 14.Donchin, E., Heffley, E.: Multivariate analysis of event-related potential data: a tutorial review. In: Otto, D. (ed.) Multidisciplinary Perspectives in Event-Related Brain Potential Research, pp. 555–572. U.S. Government Printing Office, Washington (1978)Google Scholar
- 16.Li, W., Clifton, C.: Semantic integration in heterogeneous databases using neural networks. In: Proc. 20th Intl. Conf. on Very Large Data Bases, pp. 1–12 (1994)Google Scholar
- 19.Doan, A.H., Domingos, P., Halevy, A.: Reconciling schemas of disparate data sources: a machine-learning approach. In: Proc ACM SIGMOD Conf., pp. 509–520 (2001)Google Scholar