A Nonlinear Technique for Analysis of Big Data in Neuroscience

Koel Das () and Zoran Nenadic
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Koel Das: IISER Kolkata, Department of Mathematics and Statistics
Zoran Nenadic: University of California, Department of Biomedical Engineering

A chapter in Big Data Analytics, 2016, pp 237-257 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-81-322-3628-3_13

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DOI: 10.1007/978-81-322-3628-3_13

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