Dissertation > Medicine, health > Basic Medical > Medical science in general > Biomedical Engineering > General issues > Biological information, biological control

EEG studies pay attention to the problem of the non-active space

Author QiuLiJun
Tutor YaoDeZhong
School University of Electronic Science and Technology
Course Biomedical Engineering
Keywords EEG dipole source localization involuntary visual spatial attention event-related potentials (ERP) peripheral cue linear computation model
CLC R318.04
Type Master's thesis
Year 2006
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EEG is a measurement of the time-varying potential differences between electrodes fixed on the scalp. It is used in many applications where the spatial and temporal aspects of electrical brain activities are studied. In the spatial study of EEG data, solving the inverse problem will provide us with precise information about the location, intensity and distribution of the neural current sources. In the temporal study of EEG data, event-related potentials (ERP) is widely used in the research of brain function by many researchers and recognized as‘window of brain’s advanced function’.In this thesis, the spatial and temporal analysis technique of EEG data of involuntary visual spatial attention has been studied. The main contents are as follows:1. The spatial analysis of EEG data: a novel evolutionary algorithm named particle swarm optimization (PSO) is introduced to the EEG inverse problem to localize dipole sources in the brain. Simulated cases are investigated to demonstrate the feasibility of standard PSO (SPSO). Based on the standard PSO (SPSO), we propose a hybrid PSO (HPSO) algorithm to improve the localization performance. The performance of the new algorithm is discussed and the control parameters are chose experimentally. The precision and computation speed of the new proposed algorithm are also compared with the standard PSO algorithm. Finally, the HPSO algorithm is also tested by a real EEG data of involuntary visual spatial attention experiment.2. The temporal analysis of EEG data: multiple implicit variables may be involved in limited ERP measures, so special computation techniques are needed to recover these parameters. In the ERP studies, more and more researchers realize that the inherent assumption of the traditional subtraction model if not very realistic and is not valid when the latencies of paired data are quite different. In this study, a new linear computation model is adopted to get multiple implicit variables involved in measured ERPs and the mechanism of involuntary visual spatial attention is investigated by the new model.

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