TY - JOUR
TI - Efficient feature for classification of eye movements using electrooculography signals
AU - Phukpattaranont Pornchai
AU - Aungsakul Siriwadee
AU - Phinyomark Angkoon
AU - Limsakul Chusak
JN - Thermal Science
PY - 2016
VL - 20
IS - 12
SP - 563
EP - 572
PT - Article
AB - 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.