Abstract
As an important issue of Brain Informatics (BI) methodology, systematic brain data analysis has gained significant attractions in BI community. However, the existing expert-driven multi-aspect data analysis and distributed analytical platforms excessively depend on individual capabilities and cannot be widely adopted in systematic human brain study. In this paper, we propose a provenance driven approach for systematic brain data analysis, which is implemented by using the Data-Brain, BI provenances and the Global Learning Scheme for BI. Furthermore, a systematic EEG data analysis for emotion recognition which is a key issue of affective computing is described to demonstrate significance and usefulness of the proposed approach. Such a provenance driven approach reduces the dependency of individual capabilities and provides a practical way for realizing the systematic human brain data analysis of BI methodology.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhong, N., Liu, J., Yao, Y., Wu, J., Lu, S., Qin, Y., Li, K., Wah, B.: Web intelligence meets brain informatics. In: Zhong, N., Liu, J., Yao, Y., Wu, J., Lu, S., Li, K. (eds.) WImBI 2006. LNCS (LNAI), vol. 4845, pp. 1–31. Springer, Heidelberg (2006). doi:10.1007/978-3-540-77028-2_1
Motomura, S., Zhong, N.: Multi-aspect data analysis for investigating human computation mechanism. Cogn. Syst. Res. 11(1), 3–15 (2010)
Cao, L.B., Zhang, C.Q.: The evolution of KDD: towards domain-driven data mining. Int. J. Pattern Recogn. Artif. Intell. 21(4), 677–692 (2007)
Chen, J.H., Zhong, N.: Data-brain modeling based on brain informatics methodology. In: IEEE and WIC and ACM International Conference on Web Intelligence, WI 2008, 9–12 December 2008, Sydney, pp. 41–47 (2008)
Zhong, N., Chen, J.H.: Data-brain driven multi-aspect mining process planning. In: 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–4 (2010)
Brazdil, P.: Metalearning: applications to data mining. Cognitive Technologies (2009)
Koelstra, S., Muhl, C., Soleymani, M., et al.: DEAP: a database for emotion analysis using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2011)
Klyne, G., Carroll, J.J., et al.: Resource description framework (RDF): concepts and abstract syntax. In: World Wide Web Consortium Recommendation (2004)
Garner, S.R., et al.: WEKA: the waikato environment for knowledge analysis. In: Proceedings of the NewZealand Computer Science Research Students Conference, pp. 57–64. Citeseer (1995)
Zhong, N., Bradshaw, J.M., Liu, J.M., Taylor, J.G.: Brain informatics. IEEE Intell. Syst., pp. 26–20 (2011)
Zhong, N., Chen, J.H.: Constructing a new-style conceptual model of brain data for systematic brain informatics. IEEE Trans. Knowl. Data Eng. 24(12), 2127–2142 (2012)
Simmhan, Y.L., Plale, B., Gannon, D.: A survey of data provenance in e-Science. Sigmod Rec. 3(34), 31–36 (2005)
Buneman, P., Khanna, S., Wang-Chiew, T.: Why and where: a characterization of data provenance. In: Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 316–330. Springer, Heidelberg (2001). doi:10.1007/3-540-44503-X_20
Chen, J.H., Zhong, N., Huang, R.: Towards systematic human brain data management using a data-brain based GLS-BI system. In: Yao, Y., Sun, R., Poggio, T., Liu, J., Zhong, N., Huang, J. (eds.) BI 2010. LNCS (LNAI), vol. 6334, pp. 365–376. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15314-3_35
Noy, N.F., Musen, M.A.: Specifying ontology views by traversal. In: McIlraith, S.A., Plexousakis, D., Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 713–725. Springer, Heidelberg (2004). doi:10.1007/978-3-540-30475-3_49
Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 6(39), 1161–1178 (1980)
Acknowledgments
This work is supported by National Key Basic Research Program of China (2014CB744605), National Natural Science Foundation of China (61272345), Research Supported by the CAS/SAFEA International Partnership Program for Creative Research Teams, the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (25330270).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Li, X., Yan, J., Chen, J., Yu, Y., Zhong, N. (2016). A Provenance Driven Approach for Systematic EEG Data Analysis. In: Ascoli, G., Hawrylycz, M., Ali, H., Khazanchi, D., Shi, Y. (eds) Brain Informatics and Health. BIH 2016. Lecture Notes in Computer Science(), vol 9919. Springer, Cham. https://doi.org/10.1007/978-3-319-47103-7_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-47103-7_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47102-0
Online ISBN: 978-3-319-47103-7
eBook Packages: Computer ScienceComputer Science (R0)