Development of robust electrooculography (EOG)- based human-computer interface controlled by eight- directional eye movements

Electrooculography (EOG) signal is one of the useful electro-physiological signals. The EOG signals provide information about eye movements that can be used as a control signal in human-computer interface (HCI). Usually, eight-directional movements, including up, down, right, left, up-right, up-left, down-right and down-left, are proposed. Development of the EOG signal classification has been shown more increasing interest in the last decade; however, the effect of noises on classification system is a major problem to degrade the usefulness of EOG-based HCI. A robust classification algorithm of the eight movements is proposed, in which this technique can conduct the effect of noises in EOG signal, particularly for involuntary movements and eye-blink artifacts. The proposed algorithm was based on the onset analysis, feature extraction, the first derivative technique and threshold classification. Eight beneficial time domain features were proposed including the peak and the valley amplitude positions, and the upper and the lower wavelengths of two EOG channels, vertical and horizontal channels. Based on the optimal threshold values and conditions, the results showed that classification accuracy reached 100% for three-subject testing. In addition, the first derivative technique was additionally implemented in order to avoid the eye-blink artifact and other eight time domain features, that is, peak amplitude and area under curve, have been investigated for use in advanced HCI interfaces, notably, eye activity and eye writing recognitions.