Pion-Tonachini Luca, Hsu Sheng-Hsiou, Makeig Scott, Jung Tzyy-Ping, Cauwenberghs Gert
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:4114-7. doi: 10.1109/EMBC.2015.7319299.
The Electroencephalogram (EEG) is a noninvasive functional brain activity recording method that shows promise for becoming a 3-D cortical imaging modality with high temporal resolution. Currently, most of the tools developed for EEG analysis focus mainly on offline processing. This study introduces and demonstrates the Real-time EEG Source-mapping Toolbox (REST), an extension to the widely distributed EEGLAB environment. REST allows blind source separation of EEG data in real-time using Online Recursive Independent Component Analysis (ORICA), plus near real-time localization of separated sources. Two source localization methods are available to fit equivalent current dipoles or estimate spatial source distributions of selected sources. Selected measures of raw EEG data or component activations (e.g. time series of the data, spectral changes over time, equivalent current dipoles, etc.) can be visualized in near real-time. Finally, this study demonstrates the accuracy and functionality of REST with data from two experiments and discusses some relevant applications.
脑电图(EEG)是一种非侵入性的大脑功能活动记录方法,有望成为一种具有高时间分辨率的三维皮质成像方式。目前,大多数为脑电图分析开发的工具主要集中在离线处理上。本研究介绍并演示了实时脑电图源映射工具箱(REST),它是广泛使用的EEGLAB环境的扩展。REST允许使用在线递归独立成分分析(ORICA)实时对脑电图数据进行盲源分离,以及对分离源进行近实时定位。有两种源定位方法可用于拟合等效电流偶极子或估计所选源的空间源分布。原始脑电图数据或成分激活的选定测量值(例如数据的时间序列、随时间的频谱变化、等效电流偶极子等)可以近实时可视化。最后,本研究通过两个实验的数据证明了REST的准确性和功能,并讨论了一些相关应用。