Computational Neuroengineering Approaches to Characterise Cognitive Activity in EEG Data

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.