Analysing EEG Brain Signals using Wavelet based Independent Component Analysis

This project aims to develop a software tool to automatic detect problematic signals from a specific brain disorder disease (such as epilepsy and dementia etc) from EEG recordings and help neurologists to diagnose the disease using wavelet based independent component analysis (ICA). The approach merges the advantages of wavelet decomposition and ICA. Wavelet decomposition projects EEG signals into a high-dimensional orthogonal basis where the ICA performance is significantly improved. This project will improve the quality of life of brain disorder patients through accurate diagnoses and early intervention.

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