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Towards EEG Microstate Analysis.

Ma, Lan.

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Towards EEG Microstate Analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Towards EEG Microstate Analysis./
作者:	Ma, Lan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	131 p.
附註:	Source: Dissertations Abstracts International, Volume: 79-11, Section: B.
Contained By:	Dissertations Abstracts International79-11B.
標題:	Biomedical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10805395
ISBN:	9780355820584

Towards EEG Microstate Analysis.
Ma, Lan.

Towards EEG Microstate Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 131 p.

Source: Dissertations Abstracts International, Volume: 79-11, Section: B.

Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2018.

This item must not be sold to any third party vendors.

The progress of brain imaging technology has begun to clarify the dynamics of the brain. Electroencephalography (EEG), one of the most important brain monitoring methods, non-invasively measures voltage fluctuations resulting from ionic current within the neurons of the brain. This technology has shown the potential to interpret mental processes, diagnose brain disorders, and greatly expand our knowledge of neural processes. Microstate analysis is a method which evaluates multichannel EEG recordings as a series of quasi-stable coherent activations. Each microstate is characterized by a unique topography of electric potentials along a channel array. For each time point, EEG recordings can be clustered into one topography. This technique evaluates signals recorded from all areas of the cortex simultaneously, and is thus capable of assessing large-scale brain networks' functions. Thus, microstate analysis may provide a powerful and inexpensive method with which to study states of brains. Topographies can be understood as originating from linear combination of different brain sources. The fact that there are only a limited number of topographies of microstates suggests that the brain sources obey the "Time Exclusion Property" (TEP), which states that, at each time instance, the EEG signal is dominated by one source. In this thesis, we build a mathematical model to reinterpret the concept of EEG microstates. This mathematical model reformulates microstates as a Blind Source Separation (BSS) problem, satisfying the essential "TEP" hypothesis. Furthermore, we have developed a novel BSS algorithm that meets theoretical requirements for microstate analysis. The typical BSS approaches model the underlying source signals as stochastic processes. Thus, stationarity is required in order to guarantee the existence of a representative distribution of the sources. However, EEG signals are very typically non-stationary. Our proposed algorithm offers a different approach. The BSS algorithm we developed is based on a deterministic principle: the weak exclusion principle (WEP). It makes no assumption of statistical hypotheses, making it a good candidate for an EEG signal decomposer. Our first step is to investigate algorithm performance on various kinds of simulated signals. Contrary to EEG signals, these simulated signals have ground truth. The purpose of the simulation experiment is to illustrate the proposed algorithm's accuracy and efficiency. The results show that the proposed algorithm has excellent separation performance with very fast speed. Then, we use the proposed algorithm to extract microstates from real EEG signals across different groups of subjects and paradigms, including resting state EEG and event-related EEG. The results show that the proposed algorithm can effectively separate different EEG microstates under different circumstances. Furthermore, our results showed individual microstates' consistency over time, and differences between individuals' microstates are larger than the differences between one individuals from day to day. The biometrics based on microstates (both single session and longitudinal studies) produced high identification rates. Thus, the results strongly indicate that EEG microstates can be used as powerful and reliable longitudinal features for biometrics. Most importantly, contrary to raw EEG signals, microstates directly relate to different brain states and can help to deepen our understanding of brain activities.

ISBN: 9780355820584Subjects--Topical Terms:

535387
Biomedical engineering.

Towards EEG Microstate Analysis.
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520 $a The progress of brain imaging technology has begun to clarify the dynamics of the brain. Electroencephalography (EEG), one of the most important brain monitoring methods, non-invasively measures voltage fluctuations resulting from ionic current within the neurons of the brain. This technology has shown the potential to interpret mental processes, diagnose brain disorders, and greatly expand our knowledge of neural processes. Microstate analysis is a method which evaluates multichannel EEG recordings as a series of quasi-stable coherent activations. Each microstate is characterized by a unique topography of electric potentials along a channel array. For each time point, EEG recordings can be clustered into one topography. This technique evaluates signals recorded from all areas of the cortex simultaneously, and is thus capable of assessing large-scale brain networks' functions. Thus, microstate analysis may provide a powerful and inexpensive method with which to study states of brains. Topographies can be understood as originating from linear combination of different brain sources. The fact that there are only a limited number of topographies of microstates suggests that the brain sources obey the "Time Exclusion Property" (TEP), which states that, at each time instance, the EEG signal is dominated by one source. In this thesis, we build a mathematical model to reinterpret the concept of EEG microstates. This mathematical model reformulates microstates as a Blind Source Separation (BSS) problem, satisfying the essential "TEP" hypothesis. Furthermore, we have developed a novel BSS algorithm that meets theoretical requirements for microstate analysis. The typical BSS approaches model the underlying source signals as stochastic processes. Thus, stationarity is required in order to guarantee the existence of a representative distribution of the sources. However, EEG signals are very typically non-stationary. Our proposed algorithm offers a different approach. The BSS algorithm we developed is based on a deterministic principle: the weak exclusion principle (WEP). It makes no assumption of statistical hypotheses, making it a good candidate for an EEG signal decomposer. Our first step is to investigate algorithm performance on various kinds of simulated signals. Contrary to EEG signals, these simulated signals have ground truth. The purpose of the simulation experiment is to illustrate the proposed algorithm's accuracy and efficiency. The results show that the proposed algorithm has excellent separation performance with very fast speed. Then, we use the proposed algorithm to extract microstates from real EEG signals across different groups of subjects and paradigms, including resting state EEG and event-related EEG. The results show that the proposed algorithm can effectively separate different EEG microstates under different circumstances. Furthermore, our results showed individual microstates' consistency over time, and differences between individuals' microstates are larger than the differences between one individuals from day to day. The biometrics based on microstates (both single session and longitudinal studies) produced high identification rates. Thus, the results strongly indicate that EEG microstates can be used as powerful and reliable longitudinal features for biometrics. Most importantly, contrary to raw EEG signals, microstates directly relate to different brain states and can help to deepen our understanding of brain activities.
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