The Open EEGLAB Portal Project

脑电图（EEG）是第一种功能性脑活动成像方式，具有几种天然的特征， 代谢脑成像模式的优势。脑电图是无创的，低成本，重量轻，足以 是高度移动的。关于人类电生理学的性质和使用的科学观点的两个重大转变 数据目前正在进行中。第一种是使用EEG数据作为源分辨的、相对高分辨率的 皮质源成像模式。第二个是从记录电生理数据的转变， 缺乏行为记录（例如，偶尔按下按钮的次数）到同时收集和组合 EEG数据与其他数据模态（例如，身体动作捕捉、眼动跟踪、音频和视频、ECG、EMG， GSR、MEG、fMRI等），我们称之为移动的脑/体成像（MoBI）的范例， EEGLAB信号处理环境，一个开放的， Swartz Center for Computational Neuroscience（SCCN）的源软件项目。 加州圣地亚哥（UCSD），开始作为一组EEG数据分析运行在Matlab（The Mathworks，Inc.） 1997年由Makeig发布在万维网上。EEGLAB于2001年首次从SCCN发布。现在 近20年后，EEGLAB的参考文献（Delorme & Makeig，2004）被引用了4,100多次 (now每天增加超过3个），选择加入EEGLAB讨论电子邮件列表链接超过5，500名研究人员， EEGLAB新闻列出了超过15，400名研究人员，对687名研究受访者的调查显示，EEGLAB 是认知神经科学中最广泛用于电生理数据分析的软件环境。 目前，至少有52个EEGLAB插件工具集已被其他研究人员从许多 laboratories.在这里，我们建议首先通过启用EEGLAB环境来大大增强它的功能 执行时间序列，生物物理，以及多模态和单峰EEG数据的统计分析。 然而，对大型和多模态数据集和研究进行更精确的分析， 大量的计算能力，比许多实验室中容易获得的还要多。其次，在 与圣地亚哥超级计算机中心（SDSC）合作，我们建议扩大目前的 Neuroscience Gateway（nsgportal.org）服务，使EEGLAB用户能够自由运行EEGLAB处理 SDSC超级计算机上的脚本和管道。拟议的开放EEGLAB门户网站将允许研究人员 提交任何数量的单峰或多峰EEG数据，以使用标准或定制的 EEGLAB处理管道。我们还将开发和发布第一个用于荟萃分析的工具， 源分辨EEG测量跨研究。多模态脑电分析和源级脑电分析 免费使用超级计算资源将加速EEG研究界前所未有的 观察和模拟支持人类经验和行为的分布式皮质动力学的能力。

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 second is a shift from recording electrophysiological data with at best a scant record of behavior (e.g., latencies of occasional button presses) to concurrently collecting and combining EEG data with other data modalities (e.g., body motion capture, eye tracking, audio and video, ECG, EMG, GSR, MEG, fMRI, etc.), paradigms that we term Mobile Brain/Body Imaging (MoBI) to capture brain activities and subject actions during natural, motivated behavior.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 (Delorme & Makeig, 2004) has over 4,100 citations (now increasing by over 3 per day), the opt-in EEGLAB discussion email list links over 5,500 researchers, the EEGLAB news list over 15,400 researchers, and a survey of 687 research respondents reported EEGLAB to be the software environment most widely used for electrophysiological data analysis in cognitive neuroscience. Currently, at least 52 EEGLAB plug-in tool sets have been released by other researchers from many laboratories. Here we propose, first, to greatly augment the power of the EEGLAB environment by enabling it to perform time series, biophysical, and statistical analyses of multimodal as well as unimodal EEG data. However, ever more precise analyses of large and multimodal data sets and studies require increasing amounts of computational power, more than is readily available in many laboratories. Thus second, in collaboration with the San Diego Supercomputer Center (SDSC) we propose to expand the current Neuroscience Gatew​ ay (​nsgportal.org) services to enable EEGLAB users to freely run EEGLAB processing scripts and pipelines on SDSC supercomputers. The proposed Open EEGLAB Portal will allow researchers to submit any amount of unimodal or multimodal EEG data for parallel processing using standard or custom EEGLAB processing pipelines. We will also develop and release first tools for meta-analysis of source-resolved EEG measures ​across studies. Multimodal EEG analysis and source-level EEG analysis accelerated by free use of supercomputing resources will give the EEG research community unprecedented abilities to observe and model distributed cortical dynamics supporting human experience and behavior.