Evaluating Feature Extraction Methods of Electrooculography (EOG) Signal for Human-Computer Interface

AbstractElectrooculography (EOG) signal is a widely and successfully used to detect activities of human eye. Use of the EOG signals as a control signal for human-computer interface (HCI) plays a central role in the understanding, characterization and classification of eye movements which can be applied to a wide variety of applications consisting virtual mouse and keyboard control, electric power wheelchairs and industrial assistive robots. 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, fourteen useful features extracted from two directional EOG signals: vertical (V) and horizontal (H) signals have been presented and evaluated. There are the maximum peak and valley amplitude values (PAV and VAV), the maximum peak and valley position values (PAP and VAP), the area under curve value (AUC), the number of threshold crossing value (TCV), and EOG variance (VAR), which are derived from both V and H signals. In the experiments, EOG signals obtained from three healthy subjects with eight directional eye movements were employed: up, down, right, left, up-right, up-left, down-right and down-left. The mean feature values and their standard deviations have been reported. Most features show the difference between the mean feature values. Using the analysis-of-variation test, the differences in mean features between the movements are statistically significant for ten features (p < 0.0001), particularly for the VAV, VAP, AUC, TCV and VAR of V signal, and the PAV, VAV, AUC, TCV and VAR of H signal. The combination of these features may be useful for the classification of EOG signals in both class separability and robustness point of views. Using multiple features with sufficient classifiers or threshold techniques is recommended to be evaluated in further analysis. These features can be useful for various advanced HCI applications in future researches, notably eye-exercise and eye-writing recognitions