Integration of Consonant and Pitch Processing as Revealed by the Absence of Additivity in Mismatch Negativity

Consonants, unlike vowels, are thought to be speech specific and therefore no interactions would be expected between consonants and pitch, a basic element for musical tones. The present study used an electrophysiological approach to investigate whether, contrary to this view, there is integrative processing of consonants and pitch by measuring additivity of changes in the mismatch negativity (MMN) of evoked potentials. The MMN is elicited by discriminable variations occurring in a sequence of repetitive, homogeneous sounds. In the experiment, event-related potentials (ERPs) were recorded while participants heard frequently sung consonant-vowel syllables and rare stimuli deviating in either consonant identity only, pitch only, or in both dimensions. Every type of deviation elicited a reliable MMN. As expected, the two single-deviant MMNs had similar amplitudes, but that of the double-deviant MMN was also not significantly different from them. This absence of additivity in the double-deviant MMN suggests that consonant and pitch variations are processed, at least at a pre-attentive level, in an integrated rather than independent way. Domain-specificity of consonants may depend on higher-level processes in the hierarchy of speech perception

Mathematical Foundation of Electroencephalography

Electroencephalography (EEG) has evolved over the years to be one of the primary diagnostic technologies providing information concerning the dynamics of spontaneous and stimulated electrical brain activity. The core question of EEG is to acquire the precise location and strength of the sources inside the human brain by knowledge of an electrical potential measured on the scalp. But in what way is the source recovered? Leaving aside the biological mechanisms on the cellular level responsible for the recorded EEG signals, we pay attention to the mathematical aspects of the narrative. Our goal is to provide a brief and concise introduction of the mathematical terminology associated with the modality of EEG. We start from the very beginning, presenting step by step the mathematical formulation behind EEG in a simple and clear manner, keeping the mathematical notation to a minimum. Whilst we serve only the key relations for the described problems, we focus specifically on the limitations of each modelling approach. In this fashion, the reader can appreciate the beauty of the formulas presented and discover every single piece of information encoded within these formulas

Multimodal Functional Network Connectivity: An EEG-fMRI Fusion in Network Space

EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state

A Non-Contact Electrode for Measurement of Electrocardiography

Heart disease has become a widespread epidemic threatening the health of millions of Americans and costing billions of dollars in therapy. In heart disease treatment, an effective heart monitoring technique is desired. In this thesis, a novel non-contact electrode was designed and fabricated to measure electrocardiography (ECG) based on widely used spherical volume conductor model. This model has been demonstrated to have a closed-form solution, which enables measurement of electric potential with capacitive electrode. Finite element analysis performed in Ansoft Maxwell software showed the feasibility of using an X-antenna to represent the ideal current dipole. The capacitive electrode we designed consists of two small sensing electrodes and a large reference electrode. This electrode measured promising signals for both direct and non-contact tests on spherical volume conductor model. Experiments were performed to find the best orientation and location for the electrode to measure the most significant signals on the surface of the sphere. Our electrode can also showed positive results in realizing both direct and non-contact measurement of the real ECG signals

신경전자기 신호원의 고유특성을 고려한 신호원 복원 알고리즘

학위논문 (박사)-- 서울대학교 대학원 : 협동과정 계산과학전공, 2013. 2. 정현교.뇌전도 및 뇌자도를 이용한 신경전자기 신호원 영상법은 분포전류원 모델의 경우, 추가적인 정보와 제한조건이 주어져야만 유일한 신호원을 복원할 수 있는 역문제이다. 본 학위 논문에서는 뇌전도 및 뇌자도를 이용한 신호원 영상법의 정확도를 향상시키기 위한 새로운 방법을 제안한다. 뇌자도는 대뇌피질상에 존재하는 반지름 방향의 신호원에 둔감한 반면 뇌전도는 뇌자도에 비해 상대적으로 방향성에 큰 영향을 받지 않는 것으로 알려져 있다. 이러한 신호원 고유의 방향 특성은 현재까지 분포전류원 모델의 신호원 추정에 적용되지 않았다. 본 학위 논문에서는 뇌전도와 뇌자도를 동시 측정한 경우에 대해 신호원의 방향성을 고려해 대뇌피질 상에 존재하는 신호원을 복원하는 방법을 제안하였다. 기존의 뇌전도/뇌자도 신호원 영상법을 통해 복원된 신호원은 실제 신호원과 비교했을 때 한점에 집중되거나 넓은 영역에 퍼져 있다. 따라서 다양한 분포 형태를 가진 신호원의 경우 기존 복원법을 통해서는 신호원의 분포 형태를 추정하기 힘들다는 단점이 있었다. 본 학위 논문에서는 신호원의 최대값을 추정해 이러한 한계를 극복하여 신호원의 분포를 복원할 수 있는 새로운 신호원 영상법을 제안하였다. 제안된 방법들을 다양한 상황의 시뮬레이션을 통해 정확도를 평가했으며 간질환자의 데이터에 적용해 수술로 제거된 뇌부위와 뇌자도를 이용해 복원된 신호원의 위치와 분포영역을 비교하였다. 그 결과, 본 논문에서 제안한 방법들은 기존 방법에 비해 뇌자도 및 뇌전도의 국지화 정확도를 향상시켰 수 있었으며 앞으로 뇌영역 활성부위를 추정하는 의학 분야 및 역문제 연구에서 널리 사용될 것으로 기대된다.The functional imaging of neuroelectromagnetic sources of electroencephalographic (EEG) and magnetoencephalographic (MEG) based on distributed source models requires additional information and constraints on the source in order to overcome the ill-posedness and to obtain a plausible solution. In this dissertation, we present two methods to enhance accuracy of MEG and EEG source reconstruction. We propose a new cortical source imaging algorithm for integrating simultaneously recorded EEG and MEG, which takes into account the different sensitivity characteristics of the two modalities with respect to cortical source orientations. It is well known that MEG cannot reliably detect neuronal sources with radial orientation, whereas EEG is relatively less dependent on the source orientations than MEG. However, this intrinsic difference has not previously been taken into account in the integrative cortical source imaging using simultaneously recorded EEG and MEG data. On the other hands, most imaging algorithms explicitly favor either spatially more focal or diffuse current source patterns. Naturally, in a situation where both focal and extended sources are present or the source is arbitrary distributed, such reconstruction algorithms may yield inaccurate estimate. The other algorithm proposed in this dissertation improves accuracy of bio-electromagnetic source estimation regardless the extension of source distribution. The additional maximum amplitude constraint does successively enhance the localization accuracy in EEG/MEG source imaging. The proposed approaches are validated through numerical simulations and applied to practical epilepsy measurements and compared to the resection region. From the extensive analysis, it will be shown that the proposed approaches can enhance the source localization accuracy considerably, compared to the conventional approaches. Therefore the proposed methods in this dissertation are expected to be a promising approach on the research of inverse problem and many clinical applications of EEG and MEG.Abstracts 1 Contents 3 List of Tables 5 List of Figures 6 List of Symbols 8 1. Introduction 9 1.1 Motivation and Aim 9 1.2 Overview of Chapters 14 2. Basics of Functional Neuroimaging 16 2.1 Functional Neuroimaging 16 2.2 Measurment of EEG and MEG 19 2.2.1 EEG 19 2.2.2 MEG 22 2.3 Anatomy of Human Brain 24 2.4 Generation of Neuroelectromagnetic Fields 29 3. Forward and Inverse Problems 31 3.1 Neuroelectromagnetic Forward Problem 31 3.1.1 Quasi-Static Approximation 31 3.1.2 Analytic Formulation 32 3.1.3 Numerical Approach 35 3.1.4 Linearization of Forward Problem 38 3.2 Neuroelectromagnetic Inverse Problem 39 3.2.1 Distributed Source Model 39 3.2.2 L2 Norm Mminimization Approach 40 3.2.3 L1 Norm Minimization Approach 42 4. Preprocessing and Quantitative Evalution Metrics 43 4.1 Preprosessing 43 4.2 Techniques of Quantification of Distributed Source 46 5. Algorithm Considering Directional Characteristics 56 5.1 Proposed Algorithm 56 5.2 Numerical Experiment of Proposed Method 63 6. Algorithm Considering the Maximum Current Density 70 6.1 Proposed Algorithm 70 6.2 Numerical Experiment of Proposed Method 72 6.3 Application to Localization of Epileptic Zone 84 7. Conclsion 89 References 92 Appendix A. Derivation of L2 Norm Minimization Problem 100 Appendix B. Derivation of Directional Inverse Operators 105 Appendix C. Derivation of L1 Norm Minimization Problem 107 Abstract (in Korean) 110Docto

Reduction of conductivity uncertainty propagations in the inverse problem of EEG source analysis

In computer simulations, the response of a system under study depends on the input parameters. Each of these parameters can be assigned a fixed value or a range of values within the input parameter space for system performance evaluations. Starting from values of the input parameters and a certain given model, the so-called forward problem can be solved that needs to approximate the output of the system. Starting from measurements related to the output of the system model it is possible to determine the state of the system by solving the so-called inverse problem. In the case of a non-linear inverse problem, non-linear minimization techniques need to be used where the forward model is iteratively evaluated for different input parameters. The accuracy of the solution in the inverse problem is however decreased due to the noise available in the measurements and due to uncertainties in the system model. Uncertainties are parameters for which their values are not exactly known and/or that can vary in time and/or depend on the environment. These uncertainties have, for given input parameter values, an influence on the forward problem solution. This forward uncertainty propagation leads then to errors in the inverse solutions because the forward model is iteratively evaluated for recovering the inverse solutions. Until now, it was assumed that the recovery errors could not be reduced. The only option was to either quantify the uncertain parameter values as accurate as possible or to reflect the uncertainty in the inverse solutions, i.e. determination of the region in parameter space wherein the inverse solution is likely to be situated. The overall aim of this thesis was to develop reduction techniques of inverse reconstruction errors so that the inverse problem is solved in a more robust and thus accurate way. Methodologies were specifically developed for electroencephalography (EEG) source analysis. EEG is a non-invasive technique that measures on the scalp of the head, the electric potentials induced by the neuronal activity. EEG has several applications in biomedical engineering and is an important diagnostic tool in clinical neurophysiology. In epilepsy, EEG is used to map brain areas and to receive source localization information that can be used prior to surgical operation. Starting from Maxwell’s equations in their quasi-static formulation and from a physical model of the head, the forward problem predicts the measurements that would be obtained for a given configuration of current sources. The used headmodels in this thesis are multi-layered spherical head models. The neural sources are parameterized by the location and orientation of electrical dipoles. In this thesis, a set of limited number of dipole sources is used as source model leading to a well posed inverse problem. The inverse problem starts from measured EEG data and recovers the locations and orientations of the electrical dipole sources. A loss in accuracy of the recovered neural sources occurs because of noise in the EEG measurements and uncertainties in the forward model. Especially the conductivity values of scalp, skull and brain are not well known since these values are difficult to measure. Moreover, these uncertainties can vary from person to person, in time, etc. In this thesis, novel numerical methods are developed so to provide new approaches in the improvement of spatial accuracy in EEG source analysis, taking into account model uncertainties. Nowadays, the localization of the electrical activity in the brain is still a current and challenging research topic due to the many difficulties arising e.g. in the process of modeling the head and dealing with the not well known conductivity values of its different tissues. Due to uncertainty in the conductivity value of the head tissues, high values of errors are introduced when solving the EEG inverse problem. In order to improve the accuracy of the solution of the inverse problem taking into account the uncertainty of the conductivity values, a new mathematical approach in the definition of the cost function is introduced and new techniques in the iterative scheme of the inverse reconstruction are proposed. The work in this thesis concerns three important phases. In a first stage, we developed a robust methodology for the reduction of errors when reconstructing a single electrical dipole in the case of a single uncertainty. This uncertainty concerns the skull to soft tissue conductivity ratio which is an important parameter in the forward model. This conductivity ratio is difficult to quantify and depends from person to person. The forward model that we employed is a three shell spherical head model where the forward potentials depend on the conductivity ratio. We reformulated the solution of the forward problem by using a Taylor expansion around an actual value of the conductivity ratio which led to a linear model of the solution for the simulated potentials. The introduction of this expanded forward model, led to a sensitivity analysis which provided relevant information for the reconstruction of the sources in EEG source analysis. In order to develop a technique for reducing the errors in inverse solutions, some challenging mathematical questions and computational problems needed to be tackled. We proposed in this thesis the Reduced Conductivity Dependence (RCD) method where we reformulate the traditional cost function and where we incorporated some changes with respect to the iterative scheme. More specifically, in each iteration we include an internal fitting procedure and we propose selection of sensors. The fitting procedure makes it possible to have an as accurate as possible forward model while the selection procedure eliminates the sensors which have the highest sensitivity to the uncertain skull to brain conductivity ratio. Using numerical experiments we showed that errors in reconstructed electrical dipoles are reduced using the RCD methodology in the case of no noise in measurements and in the case of noise in measurements. Moreover, the procedure for the selection of electrodes was thoroughly investigated as well as the influence of the use of different EEG caps (with different number of electrodes). When using traditional reconstruction methods, the number of electrodes has not a high influence on the spatial accuracy of the reconstructed single electrical dipole. However, we showed that when using the RCD methodology the spatial accuracy can be even more increased. This because of the selection procedure that is included within the RCD methodology. In a second stage, we proposed a RCD method that can be applied for the reconstruction of a limited number of dipoles in the case of a single uncertainty. The same ideas were applied onto the Recursively Applied and Projected Multiple Signal Classification (RAP-MUSIC) algorithm. The three shell spherical head model was employed with the skull to brain conductivity ratio as single uncertainty. We showed using numerical experiments that the spatial accuracy of each reconstructed dipole is increased, i.e. reduction of the conductivity dependence of the inverse solutions. Moreover, we illustrated that the use of the RCD-based subspace correlation cost function leads to a high efficiency even for high noise levels. Finally, in a third stage, we developed a RCD methodology for the reduction of errors in the case of multiple uncertainties. We used a five shell spherical head model where conductivity ratios with respect to skull, cerebrospinal fluid, and white matter were uncertain. The cost function as well as the fitting and selection procedure of the RCD method were extended. The numerical experiments showed reductions in the reconstructed electrical dipoles in comparison with the traditional methodology and also compared to the RCD methodology developed for dealing with a single uncertainty