Electrooculography (EOG) signal is widely and successfully used to detect
activities of human eye. The advantages of the EOG-based interface over other
conventional interfaces have been presented in the last two decades; however,
due to a lot of information in EOG signals, the extraction of useful features
should be done before the classification task. In this study, an efficient
feature extracted from two directional EOG signals: vertical and horizontal
signals has been presented and evaluated. There are the maximum peak and
valley amplitude values, the maximum peak and valley position values, and
slope, which are derived from both vertical and horizontal signals. In the
experiments, EOG signals obtained from five healthy subjects with ten
directional eye movements were employed: up, down, right, left, up-right,
up-left, down-right down-left clockwise and counterclockwise. The mean
feature values and their standard deviations have been reported. The
difference between the mean values of the proposed feature from different eye
movements can be clearly seen. Using the scatter plot, the differences in
features can be also clearly observed. Results show that classification
accuracy can approach 100% with a simple distinction feature rule. The
proposed features can be useful for various advanced human-computer interface
applications in future researches.
Efficient feature for classification of eye movements using electrooculography signals
