EEGLAB: Software for Analysis of Human Brain Dynamics

脑电图（EEG），第一个功能脑活动成像模式，有几个天然的优势， 代谢脑成像模式。EEG是非侵入性的，低成本的，并且足够轻以高度移动的。两 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EEG数据作为源分辨的、相对高分辨率的皮层源成像模态的转变。的 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）开始时是一组在 Matlab（The Mathworks，Inc.）1997年由Makeig发布在万维网上。EEGLAB首次发布于 2001年，SCCN。现在近二十年后，EEGLAB参考文献[4]有超过6，750次引用（现在正在增加 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 2011年，一项对687名研究受访者进行的独立调查报告称，EEGLAB是 在认知神经科学中，最广泛用于电生理数据分析的环境。我们的统计数据显示， 在过去的四年里，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 我们的实验室收集的数据，以建立优化的自动化数据处理管道。我们将大大增强 通过提供交叉研究荟萃分析功能来增强EEGLAB环境，并将修改软件架构， 使用与首次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。这些工具将整合HED注释系统，允许跨 大量的研究资料。我们将在EEGLAB中实现波束形成。我们将开发一个分层贝叶斯 为跨学科和研究的多项措施的有效来源进行聚类的框架，并将开发工具， 在这些尺度上对信息流措施进行统计测试。虽然EEG和MEG记录有共同点， 存在了四十年，很少有可用的软件可以结合联合收割机这两种数据类型，同时记录（“MEEG”数据）， 加强源头分离。我们最近表明，伊卡分解也允许联合MEEG有效源 MEG和联合MEEG数据分解和成像将集成到EEGLAB工具集。我们 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 源动力学这些根本性的改进将进一步使用非侵入性的人体电生理学的3-D 在美国和世界范围内进行功能性皮层脑成像，从而加速了非侵入性基础和 临床人脑研究使用高时间和空间分辨的脑电磁动力学措施。

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.