Browsing by Author "Du Toit, W. B."
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- ItemEEG artefact removal methods compared using semi-synthetic data for the analysis of ADHD EEG-Data.(2021-12) Du Toit, W. B.; Venter, M. P.; Van den Heever, D.; Stellenbosch University. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering.ENGLISH ABSTRACT: The electroencephalogram (EEG) is a measure of the biological electrical sig- nals that reflect the brain’s functional state, allied to a person’s mental condi- tion and nervous system activity. EEG is, however, an extremely weak signal, thus easily contaminated by artefacts. Artefacts are activities that do not di- rectly originate from the brain but are still present in the EEG data. Artefacts significantly complicate, distort and obscure the analysis of the data originat- ing exclusively from the brain. This thesis aimed to identify, test, and automate robust methods for remov- ing EEG artefacts. This aim was achieved by developing a simulated dataset, based on real ‘clean’ EEG and artefacts, namely a semi-synthetic dataset with significant variation in types, forms, intensity and combinations of physiolog- ical artefacts. This dataset was used to test the effectiveness and efficiency of three blind source separation (BSS) techniques, namely Extended Infomax, second-order blind identification (SOBI), canonical correlation analysis (CCA) and a developed auto threshold method. Additionally, the BSS methods were fully automated, using a novel but simple approach, to prevent the preprocess- ing of EEG data from becoming a bottleneck for data analysis. The semi-synthetic dataset consisted of ‘clean’ EEG datasets, which was contaminated separately and together by electrocardiography (ECG), elec- trooculography (EOG) and electromyography (EMG) artefacts varied for each EEG dataset. This thesis compared the time-series, topography, amplitude spectra, and the signal-to-noise ratio (SNR) characteristics of real ‘clean’ EEG and artefacts, found in the relevant literature, to the characteristics of the semi-synthetic dataset developed, for the purpose of validation. BSS techniques were used because they were the most popular for artefact removal. Furthermore, independent component analysis (ICA) was identified as the most popular BSS subcategory. The two most popular ICA methods, namely Extended Infomax and SOBI, were tested along with CCA. In addi- tion, an auto threshold method, using the standard deviation of the data, was created and tested on the data. The effectiveness of the cleaning methods was determined and compared using the SNR increase of the contaminated data. The efficiency was deter- mined and compared using the average time each BSS method took to identify the components and for the auto threshold method took identify all the arte- fact ranges. The SNR and time results were further analysed using boxplots and t-tests. With the removal of EOG artefacts, CCA was the most effective. Extended Infomax was the most effective with the removal of EMG artefacts. With the removal of ECG artefacts, SOBI outperformed the other methods in terms of effectiveness. Furthermore, when combining all three artefacts, the effec- tiveness of the BSS methods was less distinguishable, having closer P values, with Extended Infomax being the most effective. The auto threshold method showed comparable effectiveness results to the BSS methods, but in terms of efficiency, it was about 10, 20 and 100 times faster than CCA, Extended Info- max and SOBI, respectively. Concerning the automation of the BSS methods, the fully automated and semi-automatic Extended Infomax methods showed no significant difference, based on a t-test, in the effectiveness of EOG removal.