Online vigilance analysis based on electrooculography

This study provides a highly efficient online method for vigilance analysis and verifies this theory in some experiments. Compared with electroencephalogram (EEG) signals, electrooculography (EOG) signals are easier to collect and faster to process. Some research has proven relations between vigilance and EOG features like blink features and slow eye movement (SEM). This study uses 48 kind of features of eye blinks, SEM and rapid eye movement (REM) from horizontal and vertical channels of EOG signals. It is verified by experiments that the precision of this method is higher than other methods which uses single kind of features like eye blinks. This study also implements an online vigilance analysis method and its precision is close to the offline method after about one minute from the beginning of collecting signals. With the application of dry electrode amplifiers, this algorithm is useful in real-time vigilance estimation in practical environment. This method can be an important part of brain-machine interfaces.