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A Provenance Driven Approach for Systematic EEG Data Analysis

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Brain Informatics and Health (BIH 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9919))

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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.

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Notes

  1. 1.

    http://pipeline.loni.ucla.edu.

  2. 2.

    http://www.ibm.com/analytics/us/en/technology/spss/.

  3. 3.

    http://sccn.ucsd.edu/eeglab/.

  4. 4.

    http://www.mathworks.com.

  5. 5.

    http://www.biosemi.com.

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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

  1. Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, 100124, China

    Xian Li, Jianzhuo Yan, Yongchuan Yu & Ning Zhong

  2. College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100024, China

    Xian Li, Jianzhuo Yan & Yongchuan Yu

  3. International WIC Institute, Beijing University of Technology, Beijing, 100024, China

    Jianhui Chen & Ning Zhong

  4. Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China

    Ning Zhong

  5. Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China

    Jianhui Chen & Ning Zhong

  6. Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, 371-0816, Japan

    Ning Zhong

Authors
  1. Xian Li
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  2. Jianzhuo Yan
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  3. Jianhui Chen
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  4. Yongchuan Yu
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  5. Ning Zhong
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Corresponding author

Correspondence to Xian Li .

Editor information

Editors and Affiliations

  1. George Mason University, Fairfax, Virginia, USA

    Giorgio A. Ascoli

  2. Department of Modeling, Analysis and Theory, Allen Institute for Brain Science, Seattle, Washington, USA

    Michael Hawrylycz

  3. University of Nebraska Omaha, Omaha, Nebraska, USA

    Hesham Ali

  4. Information Science and Technology, University of Nebraska Omaha, Omaha, Nebraska, USA

    Deepak Khazanchi

  5. College of Information Sciences and Technology, University of Nebraska Omaha, Omaha, Nebraska, USA

    Yong Shi

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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

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  • DOI: https://doi.org/10.1007/978-3-319-47103-7_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47102-0

  • Online ISBN: 978-3-319-47103-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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