Blink: A Fully Automated Unsupervised Algorithm for Eye-Blink Detection in EEG Signals

Eye-blinks are known to substantially contaminate EEG signals, and thereby severely impact the decoding of EEG signals in various medical and scientific applications. In this work, we consider the problem of eye-blink detection that can then be employed to reliably remove eye-blinks from EEG signals. We propose a fully automated and unsupervised eyeblink detection algorithm, Blink that self-learns user-specific brainwave profiles for eye-blinks. Hence, Blink does away with any user training or manual inspection requirements. Blink functions on a single channel EEG, and is capable of estimating the start and end timestamps of eye-blinks in a precise manner. We collect four different eye-blink datasets and annotate 2300+ eye-blinks to evaluate the robustness performance of Blink across headsets (OpenBCI and Muse), eye-blink types (voluntary and involuntary), and various user activities (watching a video, reading an article, and attending to an external stimulation). The Blink algorithm performs consistently with an accuracy of over 98% for all the tasks with an average precision of 0.934. The source code and annotated datasets are released publicly for reproducibility and further research. To the best of our knowledge, this is the first ever annotated eye-blink EEG dataset released in the public domain.