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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
Brinkhoff, T., Kriegel, H.-P., Seeger, B.: Efficient processing of spatial joins using r-trees. In: SIGMOD ’93: Proceedings of the 1993 ACM SIGMOD international conference on Management of data, pp. 237–246. ACM, New York (1993)
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)
Faloutsos, C., Seeger, B., Traina, A., Traina Jr., C.: Spatial join selectivity using power laws. In: SIGMOD ’00: Proceedings of the 2000 ACM SIGMOD international conference on Management of data, pp. 177–188. ACM, New York (2000)
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)
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)
Keogh, E., Kasetty, S.: On the need for time series data mining benchmarks: A survey and empirical demonstration. Data Min. Knowl. Discov. 7(4), 349–371 (2003)
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)
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)
Larson, J., Navathe, S., Elmasri, R.: A theory of attributed equivalence in databases with application to schema integration. IEEE Transactions on Software Engineering 15(4), 449–463 (1989)
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)
Gratton, G., Coles, M.G.H., Donchin, E.: A procedure for using multi-electrode information in the analysis of components of the event-related potential: Vector filter. Psychophysiology 26(2), 222–232 (1989)
Spencer, K.M., Dien, J., Donchin, E.: A componential analysis of the ERP elicited by novel events using a dense electrode array. Psychophysiology 36, 409–414 (1999)
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)
Picton, T.W., Bentin, S., Berg, P., et al.: Guidelines for using human event-related potentials to study cognition: recording standards and publication criteria. Psychophysiology 37(2), 127–152 (2000)
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)
Li, W., Clifton, C.: SemInt: a tool for identifying attribute correspondences in heterogeneous databases using neural network. Data Knowl. Eng. 33(1), 49–84 (2000)
Rahm, E., Bernstein, P.: A survey of approaches to automatic schema matching. The VLDB Journal 10(4), 334–350 (2001)
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)
LePendu, P., Dou, D., Frishkoff, G., Rong, J.: Ontology Database: A New Method for Semantic Modeling and an Application to Brainwave Data. In: Ludäscher, B., Mamoulis, N. (eds.) SSDBM 2008. LNCS, vol. 5069, pp. 313–330. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, H., Frishkoff, G., Frank, R., Dou, D. (2010). Ontology-Based Mining of Brainwaves: A Sequence Similarity Technique for Mapping Alternative Features in Event-Related Potentials (ERP) Data. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2010. Lecture Notes in Computer Science(), vol 6119. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13672-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-13672-6_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13671-9
Online ISBN: 978-3-642-13672-6
eBook Packages: Computer ScienceComputer Science (R0)