NYU researchers track the brain’s cognitive arousal states from skin recordings
The human body can be viewed as a complex collection of interrelated control systems. All these systems quietly function to maintain different variables or states within our bodies. Now the states themselves aren’t always easily accessible, but the physiological changes and signals they give rise to, in a number of cases, are. Hence, one can view the human body through the lens of control theory, and this allows the mathematical tools typically used in control engineering to be applied to understanding physiology.
This is indeed the case with certain states such as emotion, cognition and energy — the values themselves aren’t easily accessible, but the changes in sweat secretions, heart rate and hormone secretions can indeed be measured. Consequently, we can use tools from control systems to estimate these unknown quantities.
In several cases, the observations to which the underlying state variables are related to are “spikey” or pulsatile in appearance and nature. Skin conductance is one such example. Bursts of neural activity to the sweat glands are responsible for the spiky appearance of a skin conductance signal. Likewise, the hormone cortisol — the body’s main stress hormone — is also secreted in pulses. Moreover, both cortisol and skin conductance signals can be modeled in strikingly similar ways mathematically. In the case of skin conductance and cortisol however, the signals aren’t purely spikey or pulsatile in nature, but are also accompanied by or incorporate a continuous-valued baseline or biochemical concentration level.
Previous work attempting to estimate underlying aspects of emotion from skin conductance or energy levels from cortisol have not considered this specific formulation of the observations. In our present work, we consider modeling the observations from skin conductance and cortisol as a marked point process (MPP) coupled together with a continuous-valued variable. This matches the inherent constituent components of the signals much better.
Researchers at NYU Tandon led by Rose Faghih, Associate Professor of Biomedical Engineering, developed a decoder to estimate an aspect of emotion known as sympathetic arousal from skin conductance and an energy state from cortisol measurements. The model was able to capture more subtle, fine-grained variations in the underlying states compared to estimates from some of our earlier models. Cortisol has a characteristic daytime vs. nighttime secretory pattern and their model was able to estimate energy levels consistent with these expectations. Estimates of sympathetic arousal based on skin conductance were also high during certain stressors and lower during relaxation.
They also obtained sympathetic arousal estimates that were consistent with blood flow patterns in the brain in an additional experiment as well. Here, blood flow was measured using a technique known as functional Near Infrared Spectroscopy (fNIRS). The algorithm l they developed on this occasion also had a superior capability with dealing with what is known as overfitting in comparison to our earlier methods.
D. S. Wickramasuriya, S. Khazaei, R. Kiani and R. T. Faghih, "A Bayesian Filtering Approach for Tracking Sympathetic Arousal and Cortisol-Related Energy From Marked Point Process and Continuous-Valued Observations," in IEEE Access, vol. 11, pp. 137204-137247, 2023, doi: 10.1109/ACCESS.2023.3334974
