Harnessing the power of eye tracking in brain-machine interfaces

Eye-tracking technology has advanced rapidly in recent years, suggesting that our eyes deserve more 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 such as screens, laptops, tablets and smartphones. In contrast, wearable-based systems use wearable technology to monitor and even influence brain states, offering a more practical approach to BMI development.

However, a promising alternative lies in developing a framework that decodes hidden brain states, such as interoceptive awareness, directly from eye tracking data. This advancement 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, an 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 via physiological indicators. Monitoring and understanding these internal brain states is crucial for optimizing the brain-body connection, but the challenge lies in how we access them.

One possible solution is to study interoception in the context of fear conditioning, a process in which increased arousal correlates with increased 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 as the aversive stimulus. Given the strong association between arousal and interoceptive awareness, researchers expected synchronized responses between these two states.

In this study, the research team decoded interoceptive awareness by analyzing neural activity associated with eye tracking data, specifically pupillometry measurements and eye movement patterns. At the same time, they decoded arousal states from skin conductance data.

Although it was expected that the two states would show similar responses to the electric shock, the interoceptive state of consciousness, as inferred from eye tracking data, was found to be more sensitive to the mild shocks than the arousal state, as inferred from skin conductance.

This finding underscores the potential of eye-tracking technology as a powerful psychophysiological tool for decoding interoceptive awareness, a signal that can provide important insights into brain-body interactions.

Towards Future Closed-Loop Systems: The Rise of Mindcam

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

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

One particularly promising application is the development of Mindcam, a system that integrates eye-tracking cameras into devices such as 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.

While this research is a promising 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 into wearables includes the development of Mindwatch, which uses information gathered from electrical charges in the skin to assess brain states. Mindcam could be used to complement that technology to provide even better data on how the brain responds to stress.

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