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Analyzing cognitive processes from complex neuro-physiologically based data: some lessons

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Abstract

In the past few years, due to their ability to extract multivariate correlations, machine learning tools have become more and more important for discovery of information in very complex data sets. This has had specific application to various data sets related to human brain tasks. However, this is far from a simple and direct methodology. Some of the issues involve dealing with the extreme signal to noise ratios, as well as variation between different individuals. Moreover, the huge amount of features relative to the number of data points is a challenge. As a result, in attacking these problems, we found it necessary to adapt a large variety of methodologies; chosen to overcome specific obstructions for specific problems. In this paper, we describe our experience working on several examples at the edge of capabilities of these systems and describe the various and variant methodologies we needed to overcome these sort of challenges. Hopefully these cases will serve as a guideline for other applications.

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Acknowledgments

We thank our colleagues for their discussions and generous sharing of data in these various works over the years. Specifically, we thank Hiroshi Ando, Tali Bitan, Zvia Breznitz, Omer Boehm, David Hardoon, Hananel Hazan, Stav Hertz, Asaf Gilboa, Ester Koilis, Rafael Malach, M. Merhav, Noberto E. Nawa, Shimon Sapir, Tali Sharon, Haim Shalelashvili, Gal Star, Yael Weiss.

This work was partially supported by a grant for computational equipment by the Caesarea Rothschild Institute at the University of Haifa, and by a Hardware Grant by NVIDIA Corporation The paper was partially written during a sabbatical visit of L. Manevitz graciously hosted at the Computer Science Department, Otago University, Dunedin, New Zealand. Some of this work has appeared in the Ph.D. thesis of Alex Frid supervised by Larry Manevitz.

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Authors and Affiliations

  1. Neurocomputation Lab, Department of Computer Science, Haifa, Israel

    Alex Frid & Larry M. Manevitz

  2. The Laboratory of Clinical Neurophysiology, Faculty of Medicine, Technion, Haifa, Israel

    Alex Frid

  3. Department of Computer Science, Ariel University, Ariel, Israel

    Larry M. Manevitz

  4. Department of Computer Science, University of Otago, Dunedin, New Zealand

    Larry M. Manevitz

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  1. Alex Frid
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  2. Larry M. Manevitz
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Correspondence to Alex Frid.

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Frid, A., Manevitz, L.M. Analyzing cognitive processes from complex neuro-physiologically based data: some lessons. Ann Math Artif Intell 88, 1125–1153 (2020). https://doi.org/10.1007/s10472-019-09669-z

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