Automated and Reliable Low-Complexity SoC Design Methodology for EEG Artefacts Removal

EEG is a non-invasive tool for neurodevelopmental disorder diagnosis (NDD) and treatment. However, EEG signal is mixed with other biological signals including Ocular and Muscular artefacts making it difficult to extract the diagnostic features. Therefore, the contaminated EEG channels are often discarded by the medical practitioners which may result in less accurate diagnosis. Independent Component Analysis (ICA) and wavelet-based algorithms require reference electrodes, which will create discomfort to the patient/children and cause hindrance to the diagnosis of the NDD and Brain Computer Interface (BCI). Therefore, it would be ideal if these artefacts can be removed real time and on hardware platform in an automated fashion and denoised EEG can be used for online diagnosis in a pervasive personalised healthcare environment without the need of any reference electrode. In this thesis we propose a reliable, robust and automated methodology to solve the aforementioned problem and its subsequent hardware implementation results are also presented. 100 EEG data from Physionet, Klinik fur Epileptologie, Universitat Bonn, Germany, Caltech EEG databases and 3 EEG data from 3 subjects from University of Southampton, UK have been studied and nine exhaustive case studies comprising of real and simulated data have been formulated and tested. The performance of the proposed methodology is measured in terms of correlation, regression and R-square statistics and the respective values lie above 80%, 79% and 65% with the gain in hardware complexity of 64.28% and hardware delay 53.58% compared to state-ofthe art approach. We believe the proposed methodology would be useful in next generation of pervasive healthcare for BCI and NDD diagnosis and treatment