Breaking the Camel's Back: Can Cognitive Overload be Quantified in the Human Brain?

Reductionism lies at the heart of science, yet this pre-occupation with the trees may mean that cognitive science is missing the forest. Based on the assumption that individual cognitive and perceptual processes interact to form bottle-necks of processing, which, in turn, have measurable detrimental effects on human performance, whole-head continuous EEG was recorded as participants undertook baseline, mild cognitive load and heavy cognitive load tasks. Behavioral measures (reaction times and error rates) showed significant performance decrements between the mild and heavy cognitive load conditions. Graph analysis and pattern identification was then used to identify a sub-set of cortical locations reflecting significant, measurable neural differences between the mild and heavy cognitive load states. This thus lays the foundation for future research into suitable metrics for more accurately measuring degree of global cognitive load as well as practical applications such as developing simple devices for measuring cognitive load in real time

A Novel Approach for Detection and Elimination of Automorphic Graphs in Graph Databases

Abstract Graphs have become indispensable in modeling and representing complicated structured data such as proteins, chemical compounds, and XML documents. Development of graph databases for use in research and development is a well-established activity in pharmaceutical and chemical industries. Storing the graphs into large databases is a challenging task as it deals with efficient space and time management. Unlike item sets in huge transactional databases, it becomes essential to ensure the consistency of graph databases since relationships among edges of a graph are predominant. One of the necessary procedures required is a mechanism to check whether two graphs are automorphic. For graphs with more than one vertex with the same label, more than one adjacency matrix representations are possible based on the ordering of vertices with identical labels and there are possibilities that the same graph is stored more than once using different adjacency matrices, leading to adverse results in mining graph databases. Difficulty in identifying and eliminating the automorphic graphs is a challenging problem to the research community. In this paper, a proficient algorithm is devised that efficiently detects and avoids the same graph getting stored into the database. The computational time is also substantially reduced compared to the canonical labeling approach used in Frequent Subgraph Discovery algorithm. The experimental results and comparisons offer a positive response to the newly proposed algorithm

Characterisation of Cognitive Activity Using Minimum Connected Component

The concept of functional brain networks offers new and interesting avenues for studying human brain function. One such avenue, as described in the current paper, involves spanning subgraphs called Minimum Connected Components (MCC) that contain only the influential connections of such networks. This paper investigates cognitive load driven changes across different brain regions using these MCC sub-graphs constructed for different states of brain functioning under different degrees of cognitive load using the graph theoretic concept of clique. The presence of cliques signifies cohesive interconnections among the subsets of nodes in MCC that are tightly knit together. To further characterise the cognitive load state from that of the baseline state, the hemisphere wise interactions among the electrode sites are measured. The empirical analysis presented in this paper demonstrates the efficiency of the MCC based clique analysis in detecting and measuring cognitive activity with the technique presented potentially having application in the clinical diagnosis of cognitive impairments

Breaking the Camel's Back: Can Cognitive Overload be Quantified in the Human Brain?

AbstractReductionism lies at the heart of science, yet this pre-occupation with the trees may mean that cognitive science is missing the forest. Based on the assumption that individual cognitive and perceptual processes interact to form bottle-necks of processing, which, in turn, have measurable detrimental effects on human performance, whole-head continuous EEG was recorded as participants undertook baseline, mild cognitive load and heavy cognitive load tasks. Behavioral measures (reaction times and error rates) showed significant performance decrements between the mild and heavy cognitive load conditions. Graph analysis and pattern identification was then used to identify a sub-set of cortical locations reflecting significant, measurable neural differences between the mild and heavy cognitive load states. This thus lays the foundation for future research into suitable metrics for more accurately measuring degree of global cognitive load as well as practical applications such as developing simple devices for measuring cognitive load in real time

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