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

Alex Frid; Larry M. Manevitz

doi:10.1007/s10472-019-09669-z

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

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	1125-1153
Number of pages	29
Journal	Annals of Mathematics and Artificial Intelligence
Volume	88
Issue number	11-12
DOIs	https://doi.org/10.1007/s10472-019-09669-z
State	Published - 1 Dec 2020
Externally published	Yes

Bibliographical note

Funding Information:
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.

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Keywords

Analysis of cognitive processes
Brain correlates
Classification
Feature selection
Machine learning

ASJC Scopus subject areas

Artificial Intelligence
Applied Mathematics

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10.1007/s10472-019-09669-z

Cite this

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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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doi = "10.1007/s10472-019-09669-z",

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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