Eye Blink-Driven EEG: A Step Towards Improved Real-World Data Classification

Recent developments in cognitive neuroscience have emphasized the use of naturalistic experimental paradigms, especially for real-world tasks like driving. This research introduced a blink-locked EEG segmentation method and contrasted its efficacy with traditional EEG segmentation. For three difficulty levels of proactive and reactive driving, we show a significant improvement in classification accuracy using a multi-classifier SVM with the blink-locked method, indicating enhancements of 4.3% for proactive driving and 4.4% for reactive driving. These findings underscore the potential of leveraging physiological markers, such as eye blinks, to enhance EEG data segmentation and deepen our understanding of cognitive dynamics in real-life scenarios.