Electrooculogram (EOG) | How Can Your Eye Movements Reveal Brain Activity?

An Electrooculogram (EOG) is a non-invasive technique used to measure the resting electrical potential between the front and back of the human eye. The eye functions like a small biological battery with a positive pole at the cornea (the front surface) and a negative pole at the retina (the back surface). This is known as the corneo-retinal potential. When the eye moves, this electrical dipole rotates, causing a shift in the electrical field around the eye. EOG records these shifts using small electrodes placed on the skin around the eyes—typically to the left and right of the eyes to detect horizontal movement, and above and below one eye to detect vertical movement. As the positively charged cornea moves closer to one electrode and further from another, the difference in voltage between the electrodes is measured. This measurement directly corresponds to the angle and velocity of the eye's rotation. The procedure is painless and safe, providing a continuous record of eye movements over time without needing to have the eyes open.

The primary information derived from an EOG is the measurement of eye movement, particularly its direction and speed. This data is invaluable in several fields. In sleep medicine, EOG is a critical component of polysomnography (the comprehensive sleep study), where it is used to identify the Rapid Eye Movement (REM) stage of sleep, characterized by fast, conjugate eye movements. In ophthalmology, it helps diagnose degenerative retinal diseases by assessing the health of the retinal pigment epithelium. Furthermore, EOG signals are utilized in human-computer interaction (HCI) systems. Because the electrical signals can be detected even when the eyes are closed, they can be translated into commands to control external devices, offering a pathway for communication and control for individuals with severe motor impairments.

In a clinical context, EOG is essential for sleep analysis. During a polysomnography test, EOG channels record the characteristic rapid, jerky eye movements that define the REM stage of sleep. The presence, absence, or abnormality of REM sleep is a key diagnostic marker for various sleep disorders. For instance, in REM sleep behavior disorder, the muscle atonia (paralysis) that normally accompanies REM sleep is absent, and EOG helps confirm the sleep stage during which physical activity occurs. For narcolepsy, EOG helps identify the abnormal transition into REM sleep directly from wakefulness. Therefore, EOG provides objective, physiological data crucial for the accurate diagnosis and management of sleep-related conditions.

EOG and video-based eye tracking both measure eye movements, but they operate on different principles and have distinct applications. EOG measures the electrical potential generated by the eye's dipole and is excellent at capturing large-scale movements (saccades) and movement velocity, even when the eyelids are closed. Its temporal resolution is high, but its spatial accuracy for determining the exact point of gaze is limited. In contrast, video-based eye tracking uses cameras to record an image of the eye, identifying the position of the pupil and corneal reflection to calculate the precise gaze point. This method offers superior spatial accuracy but typically requires the eyes to be open and can be sensitive to head movements and lighting conditions.

Yes, EOG is a significant technology in the field of Human-Computer Interfaces (HCI) and Brain-Computer Interfaces (BCI). The distinct electrical patterns generated by specific eye movements—such as looking left, right, up, down, or blinking deliberately—can be converted into discrete commands by a computer algorithm. This allows a user to control devices without physical movement. For individuals with conditions like amyotrophic lateral sclerosis (ALS) or locked-in syndrome, an EOG-based BCI can be a vital communication tool, enabling them to operate a computer cursor, type on a virtual keyboard, or steer a motorized wheelchair. The technology is relatively low-cost and computationally less demanding than EEG-based BCIs, making it a practical solution for assistive applications.