EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing

We describe a set of complementary EEG data collection and processing tools recently developed at the Swartz Center for Computational Neuroscience (SCCN) that connect to and extend the EEGLAB software environment, a freely available and readily extensible processing environment running under Matlab. The new tools include (1) a new and flexible EEGLAB STUDY design facility for framing and performing statistical analyses on data from multiple subjects; (2) a neuroelectromagnetic forward head modeling toolbox (NFT) for building realistic electrical head models from available data; (3) a source information flow toolbox (SIFT) for modeling ongoing or event-related effective connectivity between cortical areas; (4) a BCILAB toolbox for building online brain-computer interface (BCI) models from available data, and (5) an experimental real-time interactive control and analysis (ERICA) environment for real-time production and coordination of interactive, multimodal experiments

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

Preparing Laboratory and Real-World EEG Data for Large-Scale Analysis: A Containerized Approach.

Large-scale analysis of EEG and other physiological measures promises new insights into brain processes and more accurate and robust brain-computer interface models. However, the absence of standardized vocabularies for annotating events in a machine understandable manner, the welter of collection-specific data organizations, the difficulty in moving data across processing platforms, and the unavailability of agreed-upon standards for preprocessing have prevented large-scale analyses of EEG. Here we describe a "containerized" approach and freely available tools we have developed to facilitate the process of annotating, packaging, and preprocessing EEG data collections to enable data sharing, archiving, large-scale machine learning/data mining and (meta-)analysis. The EEG Study Schema (ESS) comprises three data "Levels," each with its own XML-document schema and file/folder convention, plus a standardized (PREP) pipeline to move raw (Data Level 1) data to a basic preprocessed state (Data Level 2) suitable for application of a large class of EEG analysis methods. Researchers can ship a study as a single unit and operate on its data using a standardized interface. ESS does not require a central database and provides all the metadata data necessary to execute a wide variety of EEG processing pipelines. The primary focus of ESS is automated in-depth analysis and meta-analysis EEG studies. However, ESS can also encapsulate meta-information for the other modalities such as eye tracking, that are increasingly used in both laboratory and real-world neuroimaging. ESS schema and tools are freely available at www.eegstudy.org and a central catalog of over 850 GB of existing data in ESS format is available at studycatalog.org. These tools and resources are part of a larger effort to enable data sharing at sufficient scale for researchers to engage in truly large-scale EEG analysis and data mining (BigEEG.org)

A Comparison of Neuroelectrophysiology Databases

As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN Initiative (DABI), Distributed Archives for Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. These archives provide researchers with tools to store, share, and reanalyze neurophysiology data though the means of accomplishing these objectives differ. The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are utilized by these archives to make data more accessible to researchers by implementing a common standard. While many tools are available to reanalyze data on and off the archives' platforms, this article features Reproducible Analysis and Visualization of Intracranial EEG (RAVE) toolkit, developed specifically for the analysis of intracranial signal data and integrated with the discussed standards and archives. Neuroelectrophysiology data archives improve how researchers can aggregate, analyze, distribute, and parse these data, which can lead to more significant findings in neuroscience research.Comment: 25 pages, 8 figures, 1 tabl

From the analysis of the brain images to the study of brain networks using functional connectivity and multimodal brain signals.

23(2):115-

Magnetic Field Effects On The Neuroprocessing Of Pain

Magnetic fields can affect behaviour in a variety of ways, in a manner that is dependent on the particulars of the magnetic field exposure. A specific pulsed magnetic field with analgesic properties was investigated using functional magnetic resonance imaging with acute thermal pain. The functional activation of pain was significantly different pre/post exposure vs. a sham condition within areas of the brain associated with the affective component of pain, in particular the anterior cingulate and the right insula. Sleep was found to be a significant confound with a 45-minute exposure. This was the first time fMRI has been used as a tool to investigate bioelectromagnetics effects, and demonstrates that an MR system can be used for both image acquisition and exposure. This technique will have applications to functional tasks beyond the acute thermal pain tested here