Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/56950
Title: Computational Neuroengineering Approaches to Characterise Cognitive Activity in EEG Data
Contributor(s): Nandagopal, D (author); Vijayalakshmi, R (author); Cocks, Bernie  (author)orcid ; Dahal, Nabaraj (author); Dasari, Naga (author); Thilaga, M (author)
Publication Date: 2015
DOI: 10.1007/978-3-319-13545-8_8
Handle Link: https://hdl.handle.net/1959.11/56950
Abstract: 

The human brain is one of the most complex and adaptive systems available to society. The brain consists of tens of billion of neurons (processing nodes) and over 100 trillion interconnections. This makes it an extremely complex communication network. The brain functions at a neuronal level have been explored and understood. However, at a systems level, the brain functions relating to "self awareness, conscience, emotion, intelligence, and judgment" still puzzles scientists today. Identifying the integrative aspects of brain structure and function, specifically how the connections and interactions among neuronal elements (neurons, synapses, cerebellum and contextual regions) result in cognition and behavior, is one of the last great frontiers for scientific research. Unraveling the activity of the brain's billions of neurons and how they combine to form functional networks has been constrained to behavioural observations. It remains further restricted by both technological and ethical constraints" thus, researchers are increasingly turning to sophisticated data search techniques to unravel hidden complexity. Techniques including complex network clustering and graph mining algorithms can be used to further delve into the hidden workings of the human mind. Combining these techniques with advanced signal processing techniques, inferential statistics can be used to support efficient visualization techniques to help researchers unfold and discover hidden patterns and functionality of brain networks. The objective of this chapter is to present an overview of the applications of approaches to multichannel Electroencephalography( EEG) data, bringing together a variety of techniques, including complex network analysis, linear and non-linear statistical methods. These measures include coherence, mutual information, approximate entropy, information visualization, signal processing, multivariate techniques such as the one-way ANalysis Of VAriance (ANOVA), and Post-hoc analysis procedures. The Cognitive Analysis Framework (CAF) approach outlined in this chapter aims to investigate and demonstrate the integration of these techniques and methodologies. The experiments provide deeper understanding of complex brain dynamics as well as allowing the identification of differences in system complexity, believed to underscore normal human cognition.

Publication Type: Book Chapter
Source of Publication: Knowledge-based information systems in practice, p. 115-136
Publisher: Springer
Place of Publication: Cham, Switzerland
ISBN: 9783319135441
9783319356297
9783319135458
Fields of Research (FoR) 2020: 520203 Cognitive neuroscience
Socio-Economic Objective (SEO) 2020: 280121 Expanding knowledge in psychology
HERDC Category Description: B1 Chapter in a Scholarly Book
Series Name: Smart Innovation, Systems and Technologies
Appears in Collections:Book Chapter
School of Psychology

Files in This Item:
1 files
File SizeFormat 
Show full item record

SCOPUSTM   
Citations

5
checked on Jul 6, 2024
Google Media

Google ScholarTM

Check

Altmetric


Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.