Comparison of data-driven thresholding methods using directed functional brain networks.

Over the past two centuries, intensive empirical research has been conducted on the human brain. As an electroencephalogram (EEG) records millisecond-to-millisecond changes in the electrical potentials of the brain, it has enormous potential for identifying useful information about neuronal transactions. The EEG data can be modelled as graphs by considering the electrode sites as nodes and the linear and nonlinear statistical dependencies among them as edges (with weights).

A User Interface (UI) and User eXperience (UX) evaluation framework for cyberlearning environments in computer science and software engineering education.

Despite the widespread availability and increasing use of cyberlearning environments, there remains a need for more research about their usefulness in undergraduate education, particularly in STEM education. The process of evaluating the usefulness of a cyberlearning environment is an essential measure of its success and is useful in assisting the design process and ensuring user satisfaction. Unfortunately, there are relatively few empirical studies that provide a comprehensive test of the usefulness of cyberlearning in education.

Shortest path based network analysis to characterize cognitive load states of human brain using EEG based functional brain networks.

Understanding and analyzing the dynamic interactions among millions of spatially distributed and functionally connected regions in the human brain constituting a massively parallel communication system is one of the major challenges in computational neuroscience. Many studies in the recent past have employed graph theory to efficiently model, quantitatively analyze, and understand the brain’s electrical activity. Since, the human brain is believed to broadcast information with reduced material and metabolic costs, identifying various brain regions in the shortest pathways of information dissemination becomes essential to understand the intricacies of brain function.

TVAR modeling of EEG to detect audio distraction during simulated driving.

Objective: The objective of our current study was to look for the EEG correlates that can reveal the engaged state of the brain while undertaking cognitive tasks. Specifically, we aimed to identify EEG features that could detect audio distraction during simulated driving.

Approach: Time varying autoregressive (TVAR) analysis using Kalman smoother was carried out on short time epochs of EEG data collected from participants as they undertook two simulated driving tasks.