Toward a Comprehensive Understanding of EEG and its Analyses

42 Pages Posted: 1 Jun 2017

See all articles by Armando da Rocha

Armando da Rocha

Research on Artificial and Natural Intelligence (RANI)

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Date Written: May 27, 2017

Abstract

EEG is the oldest tool for studying human cognitive function, but it is blamed by many to be useless because its poor spatial resolution despite it excellent temporal discrimination. Such comments arise from a reductionist point of view about the cerebral function. If the brain is assumed to be a distributed processing system recruiting different types of neurons and astrocytes widely locate in many distinct cerebral neighborhoods then EEG temporal resolution and the many different tools available for its analysis, turn it the tool of choice to investigate human cognition. The present paper follows this point of view. Here, to better understand the different types of information encoded in the recorded cortical electrical activity a clear model of the cortical structure and function of the different cortical column layers is presented and discussed. Taking this model into consideration, different available techniques for EEG analysis are presented and discussed under the assumption that tool combination is a necessity to provide a full comprehension of dynamics of the cortical activity supporting human cognition. Data on language understanding published elsewhere are used to illustrate how to use this combined and complex EEG analysis to disclose important details of cognitive cerebral dynamics, which reveal that cognitive neural circuits are scale free networks supporting entrainment of a large number of cortical column assemblies distributed all over the cortex. Reasoning is then assumed to result from such well orchestrated large scale entrainment.

Keywords: EEG, Factor Analisys, Event Related Activity, Regression Analysis, Language perception, Reasoning, Cyclic cerebral processing

Suggested Citation

da Rocha, Armando, Toward a Comprehensive Understanding of EEG and its Analyses (May 27, 2017). Available at SSRN: https://ssrn.com/abstract=2975753 or http://dx.doi.org/10.2139/ssrn.2975753

Armando Da Rocha (Contact Author)

Research on Artificial and Natural Intelligence (RANI) ( email )

Rua Tenente Ary Aps, 172
Jundiai, 13207-110
Brazil

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