Optimised use of independent component analysis for EEG signal processing

Zakeri, Zohreh (2017). Optimised use of independent component analysis for EEG signal processing. University of Birmingham. Ph.D.

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Abstract

Electroencephalography (EEG) is the prevalent technique for monitoring brain function. It employs a set of electrodes on the scalp to measure the electrical activity of the brain. EEG is mainly used by researchers to study the brain’s responses to a specific stimulus - the event-related potentials (ERPs). Different types of unwanted signals, which are known as artefacts, usually mix with the EEG at any point during the recording process. As the amplitudes of the EEG and ERPs are very small (in the order of microvolts), they can be buried in the artefacts which have very high amplitudes in the order of millivolts. Therefore, contamination of EEG activity by the artefacts can degrade the quality of the EEG recording and may cause error in EEG/ERP signal interpretation. Several EEG artefact removal methods already exist in the literature and these previous studies have concentrated on manual or automatic detection of either one or, of a few types of EEG artefacts. Among the proposed methods, Independent Component Analysis (ICA) based techniques are commonly applied to successfully detect the artefacts. Different types of ICA algorithms have been developed, which aim to estimate the individual sources of a linearly mixed signal. However, the estimation criterion differs across various ICA algorithms, which may deliver different results.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

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