Research Seminar - Naga Dasari

[Elyse Perin]

Dear Colleagues

 

You are invited to attend a research seminar by Naga Dasari

 

Date: Friday 16^th November 2012

 

Time: 12.30pm

 

Location: D1-05, Mawson Lakes

 

~ A light lunch will be provided ~

 

Naga Dasari,  PhD Candidate

WCL

Principal Supervisor            :       Prof. Nanda Nandagopal

Co-Supervisor                   :       Prof. Bruce Thomas

 

 

Title:

Visualisation of Complex Neural connections during Cognitive load using EEG data

Abstract:

Visualisation of complex structured data has been a challenge and studied for many years in various application domains. Electrical activity of the brain as recorded by electroencephalograph (EEG) from the human scalp collected during different states of brain functioning has gained more attention due to the enormous amounts of information hidden in the time-series data that can be efficiently modelled in frequency and in combination of time, frequency and phase domains. The emerging field of computational neuroscience seeks a network-based approach as a lens through which the neuronal activities and interactions of the complex brain network can be viewed and explored more effectively.  Our current research study is focused to investigate techniques and methodologies to identify and detect changes in brain neural connections and their clusters during cognitive load.  Advanced signal processing, computer visualisation and graph mining techniques to characterize cognitive activities in EEG during normal and cognitive load conditions are applied. 

 

Graph analysis of the EEG data discloses high degrees of correlation between certain brain regions during eyes open and cognitive load states.  Temporal analysis of graph data reveals a degree of variability of the functional networks demonstrating non-stationary nature of the EEG data.  However, it is observed that the neuronal clusters during cognitive load are distinctively different from eyes open state.  These differences can be established by computing graph metrics.  Combination of graphs and frequency spectrum analysis may throw more light on the hidden differences. 

 

 

 

 

Kind regards

Elyse