6533b851fe1ef96bd12a9ff5

RESEARCH PRODUCT

Online Detection and Removal of Eye Blink Artifacts from Electroencephalogram

subject

description

The most prominent type of artifact contaminating electroencephalogram (EEG)signals are the eyeblink (EB) artifacts, which could potentially lead tomisinterpretation of the EEG signal. Online detection and removal of eyeblink artifacts from EEG signals are essential in applications such a Brain-Computer Interfaces (BCI), neurofeedback and epilepsy diagnosis. In this thesis, algorithms that combine unsupervised eyeblink artifact detection (eADA) with enhanced Empirical Mode Decomposition (FastEMD) and Canonical Correlation Analysis (CCA) are proposed,i.e. FastEMD-CCA2 and FastCCA, to automatically identify eyeblink artifacts andremove them in an online setting. FastEMD-CCA2 and FastCCA have outperformedone of the existing state-of-the-art methods, FORCe. The average artifact removalaccuracy, sensitivity, specificity and error rate of FastEMD-CCA2 is 97.9%, 97.65%,99.22%, and 2.1% respectively, validated on a Hitachi dataset with 60 EEG signals,consisting more than 5600 eyeblink artifacts. FastCCA achieved an average of99.47%, 99.44%, 99.74% and 0.53% artifact removal accuracy, sensitivity, specificityand error rate respectively, validated on the Hitachi dataset too. FastEMD-CCA2 andFastCCA algorithms are developed and implemented in the C++ programming language to investigate the processing speed these algorithms could achieve in adifferent medium. Analysis has shown that FastEMD-CCA2 and FastCCA took about10.7 and 12.7 milliseconds respectively, on average to clean a 1-second length of EEG segment. This makes them a feasible solution for applications requiring onlineremoval of eyeblink artifacts from EEG signals.