BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis

Area of Science:

• Neuroscience
• Cognitive Science
• Biomedical Engineering

Background:

• Electroencephalography (EEG) is crucial for studying behavioral measures like blink rate and neural correlates of fatigue and attention.
• Existing EEG studies offer opportunities to characterize measure variability across tasks and subjects.
• Ocular artifacts in EEG can provide valuable insights into cognitive states.

Purpose of the Study:

• To implement an automated pipeline (BLINKER) for extracting ocular indices from EEG data.
• To characterize the variability of ocular indicators across subjects using a large EEG dataset.
• To investigate the dependence of ocular indices on task within a shooter study.

Main Methods:

• Developed an automated pipeline (BLINKER) for extracting blink rate, duration, and velocity-amplitude ratios.
• Utilized EEG and EOG channels, and/or independent components (ICs) for blink extraction.
• Applied the pipeline to over 2000 EEG datasets from eight laboratories.

Main Results:

• Successfully characterized the variability of ocular indicators across subjects in a large EEG corpus.
• Investigated the relationship between ocular indices and task demands in a shooter study.
• The BLINKER toolbox automates blink detection and analysis, providing reports and summary statistics.

Conclusions:

• The BLINKER toolbox provides an efficient and automated method for analyzing ocular indices from EEG data.
• This tool facilitates the study of fatigue, attention, and cognitive states through blink analysis.
• BLINKER enhances the characterization of EEG data variability across subjects and tasks.