EEGLAB: Software for Analysis of Human Brain Dynamics

脑电(EEG)是第一种功能性脑活动成像方式，它有几个天然的优点 代谢性脑成像模式。EEG是一种非侵入性的、低成本的、轻便的，可以高度移动的。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 是使用脑电数据作为源分辨的、相对高分辨率的皮质源成像方式的转变。这个 Swartz计算中心的开源软件项目EEGLAB信号处理环境 加州大学圣地亚哥分校(UCSD)的神经科学(SCCN)最初是一组运行在 MatLab(The Mathworks，Inc.)由Makeig于1997年在万维网上发布。EEGLAB最初是从 2001年，SCCN。近20年后的今天，EEGLAB参考论文[4]已有超过6750条引用(现在还在增加 每天超过4个)，选择加入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，2011年对687名调查受访者进行的独立调查报告称，EEGLAB是该软件 认知神经科学中最广泛用于电生理数据分析的环境。我们的统计数据显示， 在过去的四年里，EEGLAB的采用率仍然在稳步增长。在这里，我们将开发一个框架，用于 深入比较了各种预处理方法，并将机器学习方法应用于大数据量 由我们的实验室收集，以建立优化的、自动化的数据处理管道。我们将极大地增强 EEGLAB环境通过提供交叉研究元分析能力，并将修订软件体系结构以 使用与最初开发的脑成像数据结构(BIDS)框架兼容的文件和元数据组织 用于功能磁共振成像/磁共振成像数据存档。这些工具将集成HED注释系统，从而允许跨 大量研究语料库。我们将在EEGLAB内实施波束成形。我们将开发一种分层贝叶斯 为跨学科和研究的多个衡量标准的有效来源进行分类的框架，并将开发工具以 在这些规模下对信息流度量进行统计测试。尽管脑电和脑磁图记录有共同的 存在了四十年，几乎没有可用的软件可以将同时记录的两种数据类型(‘MEEG’数据)组合在一起，以 加强源头分离。我们最近发现ICA分解也允许联合脑电有效源 它将把脑磁图和联合脑电数据分解和成像整合到EEGLAB工具集中。我们 我将建立工具，使用MRI和fMRI衍生的解剖图谱来解释EEG和MEG大脑 震源动力学。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中的应用 功能性皮质脑成像在美国和世界范围内的应用，从而加快了在非侵入性基础和 临床人类大脑研究使用高时间和空间分辨率的脑电磁动力学测量方法。

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.