A Novel Wearable Forehead EOG Measurement System for Human Computer Interfaces

Abstract

Amyotrophic lateral sclerosis (ALS) patients whose voluntary muscles are paralyzed commonly communicate with the outside world using eye movement. There have been many efforts to support this method of communication by tracking or detecting eye movement. An electrooculogram (EOG), an electro-physiological signal, is generated by eye movements and can be measured with electrodes placed around the eye. In this study, we proposed a new practical electrode position on the forehead to measure EOG signals, and we developed a wearable forehead EOG measurement system for use in Human Computer/Machine interfaces (HCIs/HMIs). Four electrodes, including the ground electrode, were placed on the forehead. The two channels were arranged vertically and horizontally, sharing a positive electrode. Additionally, a real-time eye movement classification algorithm was developed based on the characteristics of the forehead EOG. Three applications were employed to evaluate the proposed system: a virtual keyboard using a modified Bremen BCI speller and an automatic sequential row-column scanner, and a drivable power wheelchair. The mean typing speeds of the modified Bremen brain–computer interface (BCI) speller and automatic row-column scanner were 10.81 and 7.74 letters per minute, and the mean classification accuracies were 91.25% and 95.12%, respectively. In the power wheelchair demonstration, the user drove the wheelchair through an 8-shape course without collision with obstacles.

A Biomimetic Soft Lens Controlled by Electrooculographic Signal

“…Because of the recognizing mechanism described above, to make use of double blink for changing focal length, a time delay around 500 ms was introduced into the system. The delay between the eye movement and the response of the system was also introduced in the previous work on EOG controlled virtual keyboard or wheelchair . The flowchart of the recognition algorithm is illustrated in Figure S7 in the Supporting Information.…”

“…Similar to previously reported signal recognizing mechanism, based on the above features of the EOG signals, we could process the EOG signals in real time through the microcontroller to determine the eye movements. By comparing the magnitude of the EOG signals with a predefined threshold values (represented by the dash lines in Figure b), we could determine whether a peak or a valley appeared.…”

“…The setup of the entire system is illustrated in Figure . The monitoring electrodes were placed in a conventional five‐electrode configuration around two eyes to acquire the EOG signals generated by the eye movements from the L‐R channel and U‐D channel as shown in Figure a. We used the Arduino microcontroller for signal processing.…”

“…Collaboration and interaction between human beings and machines or robots have been recently studied extensively and intensively . To enable such collaboration or interaction, various human–machine interfaces (HMIs) have been correspondingly designed . For example, HMIs have been developed to use electrophysiological signals, including electroencephalographic, electromyographic, and electrooculographic (EOG) signals, to control the motion of wheelchairs and diverse functions of exoskeletons .…”

“…Although in the previous studies, EOG signals have been successfully exploited for the control of the multiple degrees of freedom motions of wheelchair and robots, the current work is the first proof‐of‐concept design of using EOG signals to control soft machines. Thanks to the fast response of the DEs, the motion and deformation of the soft lens could be easily synchronized with the movements of the eyes.…”