Harnessing the power of eye tracking in brain-machine interfaces

In recent years, eye tracking technology has advanced rapidly, suggesting that our eyes deserve greater attention within the evolving brain-machine interface (BMI) landscape. One particularly intriguing area is the connection between eye movements and internal brain states—a link that is becoming increasingly difficult to ignore.

Eye tracking systems can function in a completely contactless manner, integrated into devices like screens, laptops, tablets, and smartphones. In contrast, wearable-based systems utilize wearable technology to monitor and even influence brain states, presenting a more hands-on approach to BMI development.

However, a promising alternative lies in the development of a framework that decodes hidden brain states, such as interoceptive awareness, directly from eye tracking data. This advance could help create safer, more efficient closed-loop systems that monitor and modulate the brain-body connection.

These are the findings of a new study from the lab of Rose Faghih, Associate Professor of Biomedical Engineering at NYU Tandon, now published in PNAS Nexus.

Decoding the brain’s hidden signals

Interoceptive awareness represents the brain’s ability to interpret bodily sensations—signals that arise in response to internal or external stimuli. However, these states are difficult to observe and must be decoded through physiological indicators. Tracking and understanding these internal brain states is critical for optimizing the brain-body connection, yet the challenge lies in how to access them.

One potential solution is to study interoception in the context of fear conditioning, a process where heightened arousal correlates with heightened interoception. In Pavlovian fear conditioning, subjects learn to anticipate aversive events—such as a mild electric shock—creating an ideal model for observing interoceptive signals.

In a recent experiment, participants underwent fear conditioning and extinction, with mild electric shocks used as the aversive stimulus. Given the strong association between arousal and interoceptive awareness, researchers anticipated synchronized responses between these two states.

In this study, the research team decoded interoceptive awareness by analyzing neural activity linked to eye tracking data—specifically, measurements from pupillometry and eye gaze patterns. In parallel, they decoded arousal states from skin conductance data.

While it was expected that the two states would show similar responses to the electric shock, the interoceptive awareness state, as inferred from eye tracking data, showed greater sensitivity to the mild shocks than the arousal state decoded from skin conductance.

This finding underscores the potential of eye tracking technology as a powerful psychophysiological tool for decoding interoceptive awareness, a signal that could offer significant insight into brain-body interactions.

Toward future closed-loop systems: The dawn of Mindcam

The discovery that eye tracking signals can serve as sensitive indicators of interoceptive awareness opens up exciting possibilities. These findings could pave the way for new therapeutic approaches to treating neuropsychiatric and neurodegenerative disorders.

By decoding interoceptive awareness, future closed-loop systems may be able to restore and enhance the brain-body connection, offering safer, more personalized interventions.

One particularly promising application is the development of Mindcam—a system that integrates eye-tracking-enabled cameras into devices like smartphones, tablets, and monitors. This technology could potentially monitor a user’s interoceptive awareness in real time, helping individuals regulate their mood and cognitive performance.

However, while this research represents an exciting first step, much more work is needed to develop safe and effective closed-loop systems that can reliably decode and modulate interoceptive states.

Faghih’s previous research on wearables includes the development of Mindwatch, which uses information collected from electrical charges in skin to assess brain states. Mindcam could be used to complement that technology to provide even better data on how the brain reacts to stress.

The integration of eye tracking technology into brain-machine interfaces may hold the key to unlocking deeper insights into the mind, offering new hope for improving mental health and cognitive function in the years to come.