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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