Research Experience
1. Statistical Analysis of VR measurements
The purpose of this study is to perform statistical analysis of linear and angular measurements taken from the immersive view, in a VR environment via a direct comparison to the corresponding physical measurements. The hypotheses tested are: (1) the virtual linear and angular measurements are equivalent to the physical measurements in terms of accuracy and (2) the reproducibility of virtual measurement is statistically high. The accuracy of the virtual measurement can be established with 95% confidence to be 0.5 mm for linear measurement and 0.7° for angular measurements within the scope of this study.
2. Traumatic Brain Injury (TBI) Detection using EEG sensors
3. Central Pattern Generator (CPG) to model neuronal behaviour
Previous research is based on canine gait (walk, trot, gallop) associated with neural network system and the primary objective of the project is to show rhythmic coupled oscillatory pattern behavior depending on shifting of four cells of quadruped gait using FitzHugh-Nagumo model, Runge-Kutta method (RK4) and Hopf Bifurcation.
4. Preliminary detection of breast cancer using EIS system
Goal of the project was to provide a small range of electrical signal onto the surface of human skin tissues and map corresponding impedance and abnormality of the tissue based on the impedance. However, since it was not feasible to collect breast tissue samples without government approval, we had to carry out the whole experiment with theoretical data mapping with simulating experimental data.
5. Brain Wave Pattern Detection
As an undergraduate researcher, I developed methods to extract signals from prefrontal cortex of human skull and artifacts caused by movements of the body by designing an inexpensive electroencephalogram (EEG). Acquired signal which had been extracted from the neurons, was in the microvolt range and one of the biggest challenge was to amplify the microvolt level to millivolt range while discarding noise and it has been reduced to an acceptable level (upto 40%).
As an undergraduate researcher, I developed methods to extract signals from prefrontal cortex of human skull and artifacts caused by movements of the body by designing an inexpensive electroencephalogram (EEG). Acquired signal which had been extracted from the neurons, was in the microvolt range and one of the biggest challenge was to amplify the microvolt level to millivolt range while discarding noise and it has been reduced to an acceptable level (upto 40%).