Distributed Computing Platform for EEG signal analysis

Basic info

The thesis is devoted to interpolation of observations obtained from space-distributed sensors and modelling of biomedical data using parallel processing computational technologies. The first part of this thesis describes mathematical background for EEG signal processing including discrete Fourier transform and signal filtering. It shows different interpolation methods for precisely defined coordinates of measuring centers and it provides it’s own implementation for three of them - nearest neighbor interpolation, linear interpolation and weighted distance interpolation. The last one represents the experimental approach designed specially for this thesis. The second part of this work is devoted to distributed computing engine which was implemented from scratch using Python, PHP and MATLAB programming languages. The third part contains comparison of the different cluster configurations used for distributed computing and outlines future plans.


bc_presentation.pdf [25/06/2009] Presentation


Nearest Neighbor Interpolation

Linear Interpolation

Weighted Distance Interpolation

Video Output

Nearest Neighbor Interpolation [ GIF, Xvid, x264, WMV2 ]

Linear Interpolation [ GIF, Xvid, x264, WMV2 ]

Weighted Distance Interpolation [ GIF, Xvid, x264, WMV2 ]