Computational Techniques for Characterizing Cognition Using EEG Data – New Approaches

Abstract

Identifying the integrative aspects of brain structure and function, specifically how the connections and interactions among neuronal elements (neurons, brain 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 and remains restricted by both technological and ethical constraints; thus, researchers are increasingly turning to sophisticated data search techniques such as complex network clustering and graph mining algorithms to further delve into the hidden workings of the human mind. By combining such techniques with more traditional inferential statistics and then applying these to multichannel Electroencephalography (EEG) data, it is believed that it is possible to both identify and accurately describe hidden patterns and correlations in functional brain networks, which would otherwise remain undetected. The current paper presents an overview of the application of such approaches to EEG data, bringing together a variety of techniques, including complex network analysis, coherence, mutual information, approximate entropy, computer visualization, signal processing and multivariate techniques such as the one-way analysis of variance (ANOVA). This study demonstrates that the integration of these techniques enables a depth of understanding of complex brain dynamics that is not possible by other methods as well as allowing the identification of differences in system complexity that are believed to underscore normal human cognition.