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A Nonlinear Technique for Analysis of Big Data in Neuroscience

  • Koel Das
  • Zoran Nenadic
Chapter

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.

Keywords

Linear Discriminant Analysis Neural Signal Discriminatory Information Common Spatial Pattern Neural Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Mathematics and StatisticsIISER KolkataMohanpurIndia
  2. 2.Department of Biomedical EngineeringUniversity of CaliforniaIrvineUSA

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