EEGLab: Software Analysis of Human Brain Dynamics

EEGLab：人脑动力学软件分析

• 批准号: 10737479
• 负责人: Arnaud Delorme
• 金额: $ 65.18万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10737479
• 关键词: 3-Dimensional; Acceleration; Adoption; Affect; Algorithms; Anatomy; Architecture; Bayesian Modeling; Behavior; Behavioral; Brain; Brain imaging; California; Clinic; Clinical; Cognitive; Collection; Complex; Computer software; Data; Data Analyses; Descriptor; Development; Disease; Educational process of instructing; Electroencephalography; Electronic Mail; Electrophysiology (science); Environment; Evaluation; Event; Functional Imaging; Head; Health; Human; Human body; Image; Individual; Internet; Laboratories; Learning; Life; Link; Links List; Location; Maintenance; Maps; Measures; Meta-Analysis; Metabolic; Metadata; Methods; Modality; Modeling; Modernization; Motion; Nature; Neurology; Neurosciences; Paper; Participant; Plug-in; Process; Psyche structure; Publishing; Reporting; Research; Research Methodology; Research Personnel; Resolution; Respondent; Running; Scalp structure; Scheme; Seizures; Signal Transduction; Source; Specific qualifier value; Statistical Algorithm; Structure; Surface; Surveys; Taiwan; Testing; Time; Training; United States National Institutes of Health; Universities; Visualization software; Work; annotation system; brain research; cognitive neuroscience; computational neuroscience; cost; cranium; data structure; data visualization; density; experience; graphical user interface; imaging modality; independent component analysis; innovation; interest; light weight; mathematical methods; multimodal data; multimodality; network models; news; open source; research study; signal processing; source localization; teaching laboratory; tool; tool development

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.

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、成本低且重量轻，足以用于记录 栩栩如生的情况。关于人类电生理数据的性质和使用的科学观点现在发生了重大转变 正在转变为使用 EEG 数据作为源分辨、相对高分辨率的 3D 皮层源成像模式。 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 加州大学圣地亚哥分校 (UCSD) 计算神经科学 (SCCN)。 EEGLAB 最初是作为一组 在 MATLAB (The Mathworks, Inc.) 上运行的 EEG 数据分析，由 Makeig 于 1997 年在万维网上发布。 EEGLAB 于 2001 年首次由 SCCN 发布。现在 21 年后，其参考论文 {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 新闻已超过 15,000 条，2011 年一项针对 687 名研究受访者的独立调查显示，EEGLAB 是认知神经科学中电生理数据分析最广泛使用的软件环境。脑电图实验室 引用和其他指标表明 EEGLAB 的采用仍在稳步增长。在这里，我们将大大增强力量 通过为颅内（iEEG、sEEG）和移动大脑/身体的处理提供支持来改善 EEGLAB 环境 成像（MoBI）数据（脑电图和行为），并将进一步集成用于执行高分辨率源成像的工具 来自 EEG（或 iEEG）数据。其对多模式大脑/行为记录的适用性是脑电图的优势之一 与其他成像方式相比的记录。多模式数据审查和处理工具将被纳入 EEGLAB，进一步支持移动脑成像数据处理工具的开发。我们将开发一个 使用 (1) 分层贝叶斯框架对有效源进行聚类的源连通性分析框架 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 通过波束成形进行感兴趣（ROI）动态估计。我们将进一步修改 EEGLAB 架构以使用文件和 元数据组织与脑成像数据结构（BIDS）规范兼容。这些工具将集成 分层事件描述符 (HED) 事件注释系统可实现跨数据的创新荟萃分析 多项研究。这些持续的发展将进一步促进非侵入性和（根据临床需要）侵入性的使用 人类电生理学用于 3D 功能性皮质脑成像，从而加速无创基础和 使用高度时间和空间分辨率的大脑电生理动力学测量进行临床人脑研究。

项目成果

Linking brain, mind and behavior.

• DOI: 10.1016/j.ijpsycho.2008.11.008
• 发表时间: 2009-08
• 期刊: INTERNATIONAL JOURNAL OF PSYCHOPHYSIOLOGY
• 影响因子: 3
• 作者: Makeig, Scott;Gramann, Klaus;Jung, Tzyy-Ping;Sejnowski, Terrence J.;Poizner, Howard
• 通讯作者: Poizner, Howard

Hierarchical Event Descriptors (HED): Semi-Structured Tagging for Real-World Events in Large-Scale EEG.

• DOI: 10.3389/fninf.2016.00042
• 发表时间: 2016
• 期刊: Frontiers in neuroinformatics
• 影响因子: 3.5
• 作者: Bigdely-Shamlo N;Cockfield J;Makeig S;Rognon T;La Valle C;Miyakoshi M;Robbins KA
• 通讯作者: Robbins KA

Effects of forward model errors on EEG source localization.

• DOI: 10.1007/s10548-012-0274-6
• 发表时间: 2013-07
• 期刊: BRAIN TOPOGRAPHY
• 影响因子: 2.7
• 作者: Acar, Zeynep Akalin;Makeig, Scott
• 通讯作者: Makeig, Scott

One out of ten independent components shows flipped polarity with poorer data quality: EEG database study.

• DOI: 10.1002/hbm.26540
• 发表时间: 2024-01
• 期刊: HUMAN BRAIN MAPPING
• 影响因子: 4.8
• 作者: Miyakoshi, Makoto;Kim, Hyeonseok;Nakanishi, Masaki;Palmer, Jason;Kanayama, Noriaki
• 通讯作者: Kanayama, Noriaki

Research Review: Use of EEG biomarkers in child psychiatry research - current state and future directions.

• DOI: 10.1111/jcpp.12435
• 发表时间: 2016-01
• 期刊: Journal of child psychology and psychiatry, and allied disciplines
• 作者: Loo SK;Lenartowicz A;Makeig S
• 通讯作者: Makeig S