Abstract
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/.
Highlights
Contamination of electroencephalography (EEG) by eye and muscle activity is an ongoing challenge, and many techniques exist for the removal of these artifacts (Jung et al, 2000; Delorme et al, 2007; Nolan et al, 2010; Mognon et al, 2011; Winkler et al, 2011)
We have found empirically that blink candidates with positive amplitude velocity ratio (pAVR) ≤ 3 do not correspond to normal blinks, but rather saccades having short, fast eye movements
Channels selected by BLINKER as the best in at least one dataset are labeled in Figure 4, with channels selected by only one dataset labeled in red
Summary
Contamination of electroencephalography (EEG) by eye and muscle activity is an ongoing challenge, and many techniques exist for the removal of these artifacts (Jung et al, 2000; Delorme et al, 2007; Nolan et al, 2010; Mognon et al, 2011; Winkler et al, 2011). Human performance characterization uses ocular indices to characterize fatigue and other changes in subject state (Schuri and von Cramon, 1981; Recarte et al, 2008; Benedetto et al, 2011; Wilkinson et al, 2013; McIntire et al, 2014; Marquart et al, 2015). Direct measurement of eye activity is desirable, it is possible to extract some types of ocular indices directly from EEG without additional experimental considerations. As large collections of EEG become available, these approaches enable the study of the distributions of ocular indices across many experimental conditions, diverse subject pools, and various disease conditions.
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