EOG & Saccade Detection
New research out of the University of British Columbia details how the Cyton and Gold Cup Electrodes can be used for detecting eye movements through electrooculography (EOG). EOG is a technique for measuring the corneo-retinal standing potential that exists between the front and the back of the human eye. The resulting signal is called the electrooculogram. Primary applications are in ophthalmological diagnosis and in recording eye movements. Vertical movements of the eyes are best measured by placing the electrodes on the lids, while horizontal eye movements can be best measured by placing the electrodes on the external canthi (the bone on the side of the eye).
The paper “An Oculomotor Sensing Technique for Saccade Isolation of Eye Movements Using OpenBCI” by Hiroshan Gunawardane, C.W. de Silva, and Mu Chiao presents an inexpensive and accurate signal processing technique to identify saccades, the rapid movement of the eye between fixation points. Their presented technique successfully isolated saccadic information from raw EOG signals, and showed a significant improvement with respect to other approaches (used for noise ﬁltering).
More detailed discussion of the research by the authors can be found here:
One limitation of the research by Gunawardane et. al. is that their early version of their system cannot remove artifacts caused by eye blinks.
Eye Blink Detection
Mohit Agarwal and Raghupathy Sivakumar of the Georgia Institute of Technology recently published “Blink: A Fully Automated Unsupervised Algorithm for Eye-BlinkDetection in EEG Signals.” It’s possible that this type of system could be used to assist with the artifact removal problem faced by the UBC team when collecting EOG data, and faced by others when collecting EEG signals.
Agarwal and Sivakumar believed that the first step to removing the artifacts caused by eye-blinks, was developing a sufficiently accurate algorithm that could identify blinks in the first place. The result of their work is the “BLINK” system, a fully automated and unsupervised eye-blink detection algorithm which is able to self-learn user-specific brainwave profiles for eye-blinks. There is no need for individual user training, or manual inspection and incredibly, Blink functions on a single channel of EEG. Blink accurately estimates the start and end timestamps of eye-blinks, allowing researchers to more easily account for those blinks in their datasets.
The GIT team collected 2300+ blinks using both OpenBCI and Muse across various user activities and (this is the part we love) made the source code and annotated datasets available publicly to promote reproducibility and further research.
We applaud the research done by both teams and look forward to learning more from their future work!