A/Prof Vijayalakshmi has over 17 years of academic experience and currently holds the position of A/Professor, Department of Applied Mathematics and Computational Sciences at PSG College of Technology, India. She received her Bachelor in Science (BSc), Masters of Computer Applications (MCA) and Master of Philosophy (MPhil) in Computer Science from Bharathiar University and PhD in Graph Mining Algorithms from Anna University. Her research interest includes graph based data mining, especially in complex network systems, and social network analysis. Her research publications include about 15 journal and conference papers. She has been a reviewer in the Journal of Pattern Recognition Letters, Elsevier, and the International Journal of Knowledge Engineering and Soft Data Paradigms. She has also delivered special lectures on ‘Efficient Data Structures and Algorithms for Graph based Data Mining’, Database management systems, Problem solving and C programming, and Object oriented Programming in various workshops, conferences and faculty development programme. She has undergone research training in Cognitive Neuroengineering Laboratory (CNEL) in the acquisition and analysis of brain wave data for cognitive modelling, pattern identification and visualization using graph-theoretic approaches and has established a Computational Neuroscience Laboratory at PSG College of Technology in association with Prof. Nanda Nandagopal, UniSA.
"Computational Techniques for Characterizing Cognition using EEG Data - New Approaches"
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 behaviour, is one of the last great frontiers for scientific research. Unravelling 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 now 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 multi-channel 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. This talk provides an overview of the application of such approaches to EEG data, bringing together a variety of techniques including complex network analysis, Pearson's correlation coefficient, coherence, mutual information, approximate entropy, computer visualization and signal processing techniques.