EEGLAB: Software for Analysis of Human Brain Dynamics

EEGLAB：人脑动力学分析软件

• 批准号: 10452690
• 负责人: Arnaud Delorme
• 金额: $ 54.73万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-07-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10452690
• 关键词: 3-Dimensional; Adoption; Anatomy; Architecture; Atlases; Automatic Data Processing; Automation; Bayesian Modeling; Behavior; Brain; Brain imaging; California; Classification; Clinical; Code; Cognitive; Collection; Communities; Community Outreach; Complex; Computer software; Data; Data Analyses; Data Storage and Retrieval; Databases; Development; Disease; Distributed Databases; Documentation; Educational process of instructing; Educational workshop; Electroencephalography; Electromagnetics; Electronic Mail; Electrophysiology (science); Environment; Event; Functional Imaging; Functional Magnetic Resonance Imaging; Health; Human; Image; Institutes; Internet; Joints; Laboratories; Learning; Link; Links List; Location; Machine Learning; Magnetic Resonance Imaging; Magnetoencephalography; Maintenance; Measures; Meta-Analysis; Metabolic; Metadata; Methods; Modeling; Modernization; Morphologic artifacts; Nature; Neurosciences; Newsletter; Paper; Plug-in; Process; Psyche structure; Publishing; Reporting; Research; Research Methodology; Research Personnel; Resolution; Respondent; Running; Source; Statistical Data Interpretation; Surveys; System; Testing; Time; Training; United States National Institutes of Health; Universities; Update; Visualization software; Work; archive data; archived data; automated analysis; base; brain research; central database; cognitive neuroscience; computational neuroscience; cost; data analysis pipeline; data archive; data cleaning; data infrastructure; data structure; data visualization; design; experience; graphical user interface; imaging modality; independent component analysis; interest; light weight; machine learning method; mathematical methods; news; novel strategies; online course; open source; research study; response; signal processing; statistics; support tools; teaching laboratory; tool; wiki

Electroencephalography (EEG), the first function brain activity imaging modality, has several natural advantages over metabolic brain imaging modalities. EEG is noninvasive, low cost, and lightweight enough to be highly mobile. Two major shifts in scientific perspective on the nature and use of human electrophysiological data are now ongoing. The first is a shift to using EEG data as a source-resolved, relatively high-resolution cortical source imaging modality. The EEGLAB signal processing environment, an open source software project of the Swartz Center for Computational Neuroscience (SCCN) of the University of California, San Diego (UCSD), began as a set of EEG data analysis running on Matlab (The Mathworks, Inc.) released by Makeig on the World Wide Web in 1997. EEGLAB was first released from SCCN in 2001. Now nearly twenty years later, the EEGLAB reference paper [4] has over 6,750 citations (now increasing by over 4 per day), the opt-in EEGLAB discussion email list links 6,000 researchers, the EEGLAB news list over 15,000 researchers, and an independent 2011 survey of 687 research respondents reported EEGLAB to be the software environment most widely used for electrophysiological data analysis in cognitive neuroscience. Our statistics show that after over the past four years, EEGLAB adoption is still growing steadily. Here, we will develop a framework for thorough comparison of preprocessing methods, and will apply machine learning methods on the large body of data collected by our laboratory to build optimized, automated data processing pipelines. We will greatly augment the power of the EEGLAB environment by providing a cross-study meta-analysis capability and will revise the software architecture to use a file and metadata organization compatible with the Brain Imaging Data Structure (BIDS) framework first developed for fMRI/MRI data archiving. These tools will integrate the HED annotating system allowing for meta-analysis across large corpus of studies. We will implement beamforming within EEGLAB. We will develop a hierarchical Bayesian framework for clustering effective sources on multiple measures across subjects and studies, and will develop tools to perform statistical testing on information flow measures at these scales. Although EEG and MEG recording have co- existed for four decades, little available software can combine both data types, recorded concurrently (`MEEG' data), to enhance source separation. We recently showed that ICA decomposition also allows joint MEEG effective source decomposition and will integrate MEG and joint MEEG data decomposition and imaging into the EEGLAB tool set. We will build tools to use MRI- and fMRI-derived anatomical atlases to inform the interpretation of EEG and MEG brain source dynamics. These radical improvements will further the use of non-invasive human electrophysiology for 3-D functional cortical brain imaging in the U.S. and worldwide, thereby accelerating progress in noninvasive basic and clinical human brain research using highly time- and space-resolved measures of brain electromagnetic dynamics.

脑电图（EEG）是第一个功能大脑活动成像的功能，具有多种自然优势 代谢大脑成像方式。脑电图无创，低成本且轻巧，足够轻巧。二 科学观点对人类电生理数据的性质和使用的主要转变正在进行中。第一个 是使用脑电图数据作为源分辨，相对较高的皮质源成像模式的转变。这 EEGLAB信号处理环境，Swartz计算中心的开源软件项目 加利福尼亚大学圣地亚哥分校（UCSD）的神经科学（SCCN），始于一组脑电图数据分析 MATHIG于1997年在万维网上发布的Matlab（Mathworks，Inc。）。Eeglab首次发行 SCCN在2001年。现在将近二十年后，EEGLAB参考文件[4]有6,750多个引用（现在增加 每天超过4个），选择EEGLAB讨论电子邮件列表链接6,000名研究人员，EEGLAB新闻列表超过15,000 研究人员以及2011年对687位研究受访者的独立调查报告EEGLAB是该软件 在认知神经科学中，最广泛用于电生理数据分析的环境。我们的统计数据表明 在过去的四年中，EEGLAB的采用率仍在稳步增长。在这里，我们将为 对预处理方法的详尽比较，并将在大量数据上应用机器学习方法 由我们的实验室收集以建立优化的自动数据处理管道。我们将大大增强 EEGLAB环境通过提供跨研究的荟萃分析能力，并将软件体系结构修改为 使用与大脑成像数据结构（BID）框架兼容的文件和元数据组织首先开发 用于fMRI/MRI数据归档。这些工具将集成HED注释系统，允许跨越 大量研究。我们将在EEGLAB内实施波束形成。我们将开发一个等级贝叶斯人 用于在主题和研究的多个措施上聚集有效来源的框架，并将开发工具 对这些量表的信息流量测量进行统计测试。尽管脑电图和梅格录音有共同 存在已有四十年了，几乎没有可用的软件可以将这两种数据类型合并为同时记录（“ MEEG”数据）， 增强源分离。我们最近表明，ICA分解还允许联合MEEG有效源 分解并将将MEG和联合MEEG数据分解和成像整合到EEGLAB工具集中。我们 将构建使用MRI-和FMRI和FMRI衍生的解剖图谱的工具，以告知EEG和MEG大脑的解释 源动力学。这些根本改进将进一步将非侵入性人类电生理学用于3-D 美国和全球的功能性皮质脑成像，从而加速了非侵入性基本和 临床人脑研究使用高度时间和空间分辨的脑电磁动力学测量。