Kleifges Kelly, Bigdely-Shamlo Nima, Kerick Scott E, Robbins Kay A
Department of Computer Science, University of Texas at San Antonio San Antonio, TX, USA.
Qusp Labs, Qusp San Diego, CA, USA.
Front Neurosci. 2017 Feb 3;11:12. doi: 10.3389/fnins.2017.00012. eCollection 2017.
Electroencephalography (EEG) offers a platform for studying the relationships between behavioral measures, such as blink rate and duration, with neural correlates of fatigue and attention, such as theta and alpha band power. Further, the existence of EEG studies covering a variety of subjects and tasks provides opportunities for the community to better characterize variability of these measures across tasks and subjects. We have implemented an automated pipeline (BLINKER) for extracting ocular indices such as blink rate, blink duration, and blink velocity-amplitude ratios from EEG channels, EOG channels, and/or independent components (ICs). To illustrate the use of our approach, we have applied the pipeline to a large corpus of EEG data (comprising more than 2000 datasets acquired at eight different laboratories) in order to characterize variability of certain ocular indicators across subjects. We also investigate dependence of ocular indices on task in a shooter study. We have implemented our algorithms in a freely available MATLAB toolbox called BLINKER. The toolbox, which is easy to use and can be applied to collections of data without user intervention, can automatically discover which channels or ICs capture blinks. The tools extract blinks, calculate common ocular indices, generate a report for each dataset, dump labeled images of the individual blinks, and provide summary statistics across collections. Users can run BLINKER as a script or as a plugin for EEGLAB. The toolbox is available at https://github.com/VisLab/EEG-Blinks. User documentation and examples appear at http://vislab.github.io/EEG-Blinks/.
脑电图（EEG）为研究行为指标（如眨眼频率和持续时间）与疲劳和注意力的神经关联（如θ波和α波频段功率）之间的关系提供了一个平台。此外，涵盖各种受试者和任务的脑电图研究的存在，为该领域更好地描述这些指标在不同任务和受试者之间的变异性提供了机会。我们已经实现了一个自动化管道（BLINKER），用于从脑电图通道、眼电图通道和/或独立成分（IC）中提取诸如眨眼频率、眨眼持续时间和眨眼速度-幅度比等眼部指标。为了说明我们方法的应用，我们将该管道应用于大量脑电图数据（包括在八个不同实验室采集的2000多个数据集），以描述某些眼部指标在受试者之间的变异性。我们还在一项射击者研究中调查了眼部指标对任务的依赖性。我们已经在一个名为BLINKER的免费MATLAB工具箱中实现了我们的算法。该工具箱易于使用，无需用户干预即可应用于数据集，能够自动发现哪些通道或IC捕捉到了眨眼。这些工具提取眨眼、计算常见的眼部指标、为每个数据集生成一份报告、转储单个眨眼的标记图像，并提供跨数据集的汇总统计信息。用户可以将BLINKER作为脚本运行，也可以作为EEGLAB的插件运行。该工具箱可在https://github.com/VisLab/EEG-Blinks获取。用户文档和示例见http://vislab.github.io/EEG-Blinks/。
