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A Nonlinear Technique for Analysis of Big Data in Neuroscience

In: Big Data Analytics

Author

Listed:
  • Koel Das

    (IISER Kolkata, Department of Mathematics and Statistics)

  • Zoran Nenadic

    (University of California, Department of Biomedical Engineering)

Abstract

Recent technological advances have paved the way for big data analysis in the field of neuroscience. Machine learning techniques can be used effectively to explore the relationship between large-scale neural and behavorial data. In this chapter, we present a computationally efficient nonlinear technique which can be used for big data analysis. We demonstrate the efficacy of our method in the context of brain computer interface. Our technique is piecewise linear and computationally inexpensive and can be used as an analysis tool to explore any generic big data.

Suggested Citation

  • Koel Das & Zoran Nenadic, 2016. "A Nonlinear Technique for Analysis of Big Data in Neuroscience," Springer Books, in: Saumyadipta Pyne & B.L.S. Prakasa Rao & S.B. Rao (ed.), Big Data Analytics, pages 237-257, Springer.
    • Handle: RePEc:spr:sprchp:978-81-322-3628-3_13
    DOI: 10.1007/978-81-322-3628-3_13
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