EEGLab: Software 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。脑电图是无创的，低成本的，重量轻，足以用于记录在 栩栩如生的场景现在，关于人类电生理数据的性质和使用的科学观点发生了重大转变。 正在进行的，一个转变，使用EEG数据作为源解析，相对高分辨率的3D皮质源成像模式。 EEGLAB信号处理环境是Swartz中心的一个易于扩展的开源软件项目， 计算神经科学（SCCN）的加州大学圣地亚哥分校（UCSD）。EEGLAB最初是一套 在MATLAB（The Mathworks，Inc.）上运行的EEG数据分析，1997年由Makeig发布在万维网上。 EEGLAB于2001年首次从SCCN发布。21年后的今天，它的参考文献{Delorme，2004 #1}已经结束， 18，450次引用（每天增加6.8次），其选择加入EEGLAB讨论电子邮件列表链接了6，000多名研究人员，其 EEGLAB新闻达到15，000多，2011年对687名研究受访者的独立调查报告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。EEGLAB 引用和其他指标表明，EEGLAB的采用仍在稳步增长。在这里，我们将大大增强 通过为处理颅内（iEEG、sEEG）和移动的大脑/身体提供支持， 成像（MoBI）数据（EEG和行为），并将进一步整合用于执行高分辨率源成像的工具 EEG（或iEEG）数据。它对多模态脑/行为记录的适用性是EEG的优势之一 与其他成像模式相比。多模式数据审查和处理工具将纳入 EEGLAB，以进一步支持开发用于处理移动的脑成像数据的工具。我们将开发一个 源连接性分析的框架，其使用（1）用于聚类有效源的分层贝叶斯框架 通过对跨学科和研究的多项措施进行独立成分分析确定的过程，以及（2）区域 感兴趣（ROI）动态估计。我们将进一步修改EEGLAB体系结构以使用文件， 与脑成像数据结构（BIDS）规范兼容的元数据组织。这些工具将整合 分层事件描述符（HED）事件注释系统，以实现跨数据的创新元分析， 多项研究。这些持续的发展将进一步使用非侵入性和（根据临床需要）侵入性 人体电生理学三维功能皮层脑成像，从而加快了无创基础和 临床人脑研究使用高时间和空间分辨的脑电生理动力学措施。

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 used for recording in lifelike situations. A major shift in scientific perspective on the nature and use of human electrophysiological data is now ongoing, a shift to using EEG data as a source-resolved, relatively high-resolution 3D cortical source imaging modality. The EEGLAB signal processing environment is a readily extensible open-source software project of the Swartz Center for Computational Neuroscience (SCCN) of the University of California, San Diego (UCSD). EEGLAB 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 21 years later, its reference paper {Delorme, 2004 #1} has over 18,450 citations (increasing by 6.8 per day), its opt-in EEGLAB discussion email list links over 6,000 researchers, its EEGLAB news reaches over 15,000, 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. EEGLAB citations and other metrics show that EEGLAB adoption is still growing steadily. Here, we will greatly augment the power of the EEGLAB environment by providing support for processing both intracranial (iEEG, sEEG) and mobile brain/body imaging (MoBI) data (EEG and behavior), and will further integrate tools for performing high-resolution source imaging from EEG (or iEEG) data. Its suitability for multi-modal brain/behavioral recording is one of the strengths of EEG recording compared to other imaging modalities. Multimodal data review and processing tools will be incorporated into EEGLAB, to further support the development of tools for processing mobile brain imaging data. We will develop a framework for source connectivity analysis using (1) a hierarchical Bayesian framework for clustering effective source processes identified by independent component analysis on multiple measures across subjects and studies and (2) region of interest (ROI) dynamics estimation by beamforming. We will further revise the EEGLAB architecture to use a file and metadata organization compatible with the Brain Imaging Data Structure (BIDS) specifications. These tools will integrate the Hierarchical Event Descriptor (HED) event annotation system to enable innovative meta-analyses across data from multiple studies. These continuing developments will further the use of non-invasive and (as per clinical need) invasive human electrophysiology for 3-D functional cortical brain imaging, thereby accelerating progress in noninvasive basic and clinical human brain research using highly time- and space-resolved measures of brain electrophysiological dynamics.