EEGLAB: Software for Analysis of Human Brain Dynamics

EEGLAB：人脑动力学分析软件

基本信息

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
项目类别：
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

项目摘要

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) 是第一个功能性脑活动成像模式，与代谢脑成像方式。脑电图无创、成本低、重量轻，可高度移动。二关于人类电生理数据的性质和使用的科学观点正在发生重大转变。第一个是转向使用脑电图数据作为源解析的、相对高分辨率的皮层源成像模式。这 EEGLAB 信号处理环境，Swartz 计算中心的开源软件项目加州大学圣地亚哥分校 (UCSD) 的神经科学 (SCCN) 最初是一套运行于 Matlab (The Mathworks, Inc.) 由 Makeig 于 1997 年在万维网上发布。EEGLAB 首次发布于 2001 年 SCCN。近 20 年后的今天，EEGLAB 参考论文 [4] 被引用次数超过 6,750 次（现在正在增加）每天超过 4 个），选择加入的 EEGLAB 讨论电子邮件列表链接了 6,000 名研究人员，EEGLAB 新闻列表超过 15,000 个研究人员以及 2011 年对 687 名研究受访者进行的独立调查显示，EEGLAB 是一款软件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四年过去了，EEGLAB 的采用率仍在稳步增长。在这里，我们将开发一个框架彻底比较预处理方法，并将机器学习方法应用于大量数据由我们的实验室收集，用于构建优化的自动化数据处理管道。我们将大大增强 EEGLAB 环境提供交叉研究元分析功能，并将修改软件架构以使用与首先开发的脑成像数据结构（BIDS）框架兼容的文件和元数据组织用于 fMRI/MRI 数据存档。这些工具将集成 HED 注释系统，以便进行元分析大量的研究资料。我们将在 EEGLAB 内实施波束成形。我们将开发一个分层贝叶斯跨学科和研究的多种措施的有效来源聚类框架，并将开发工具对这些规模的信息流测量进行统计测试。虽然脑电图和脑磁图记录有共同作用存在了四十年，很少有可用的软件可以结合同时记录的两种数据类型（“MEEG”数据），以加强源头分离。我们最近表明 ICA 分解还允许联合 MEEG 有效源分解并将 MEG 和联合 MEEG 数据分解和成像集成到 EEGLAB 工具集中。我们将构建工具来使用 MRI 和功能磁共振成像衍生的解剖图谱来解释脑电图和脑磁图大脑源动力。这些根本性的改进将进一步推动非侵入性人体电生理学在 3D 领域的应用功能性皮质脑成像在美国和世界范围内，从而加速非侵入性基础和使用高度时间和空间分辨率的大脑电磁动力学测量进行临床人脑研究。