Researchers at NYU Tandon have reached a key milestone of their quest to develop wearable technology that manages to measure key brain mechanisms through the skin.
Rose Faghih, Associate Professor of Biomedical Engineering, has been working for the last seven years on a technology that may measure mental activity using electrodermal activity (EDA) -; an electrical phenomenon of the skin that’s influenced by brain activity related to emotional status. Internal stresses, whether brought on by pain, exhaustion, or a very packed schedule, could cause changes within the EDA -; changes which can be directly correlated to mental states.
The overarching goal -; a Multimodal Intelligent Noninvasive brain state Decoder for Wearable AdapTive Closed-loop arcHitectures, or MINDWATCH, as Faghih calls it -; would act as a approach to monitor a wearer’s mental state, and offer nudges that will help them revert back to a more neutral frame of mind. For instance, if an individual was experiencing a very severe bout of work-related stress, the MINDWATCH could pick up on this and routinely play some relaxing music.
Now Faghih -; together with Rafiul Amin, her former PhD student -; has achieved a vital task required for monitoring this information. For the primary time, they’ve developed a novel inference engine that may monitor brain activity through the skin in real time with high scalability and accuracy. The outcomes are featured in a recent paper, “Physiological Characterization of Electrodermal Activity Enables Scalable Near Real-Time Autonomic Nervous System Activation Inference,” published in PLOS Computational Biology.
Inferring autonomic nervous system activation from wearable devices in real-time opens recent opportunities for monitoring and improving mental health and cognitive engagement.”
Rose Faghih, Associate Professor of Biomedical Engineering
Previous methods measuring sympathetic nervous system activation through the skin took minutes, which isn’t practical for wearable devices. While her earlier work focused on inferring brain activity through sweat activation and other aspects, the brand new study moreover models the sweat glands themselves. The model features a 3D state-space representation of the direct secretion of sweat via pore opening, in addition to diffusion followed by corresponding evaporation and reabsorption. This detailed model of the glands provides exceptional insight into inferring the brain activity.
The brand new model was run on data from 26 healthy individuals. The researchers showed that they’ll decipher brain signals with high reliability. Moreover, the computational power requirement of their recent algorithm is minimal and might obtain brain and physiological insights inside just a few seconds whereas one other previous approach would take minutes. Which means small, wearable monitoring technology able to incredible speed, high scalability, and extraordinary reliability is close by.
The broader impact and applications of the methodology includes performance monitoring, mental health monitoring, measuring pain and cognitive stress. Mental health tracking will help higher manage autism, post-traumatic stress disorders, excessive irritability, suicidal tendency, and more. Performance tracking and cognitive stress tracking will help improve individual productivity and quality of life.
“One’s performance changes based on their cognitive engagement and arousal levels.” says Faghih. For instance, very low or very high levels of arousal can lead to poor performance. Hence, it is anticipated that. Ultimately, researchers can utilize the inferred autonomic nervous system activation and decoded arousal to develop interventions for improving productivity.”
One example application of this method is early diagnosis of disorders like diabetic neuropathy. Small nerves transmit brain stimulation to many parts of the body, including those linked to skin conductance response. To trace the received brain activity, EDA could also be measured and monitored frequently in neuropathy-prone skin areas of the body. If a skin area has neuropathy (i.e., tiny nerves have been damaged), the brain is not going to activate that area. By monitoring changes, doctors can see how a condition like diabetic neuropathy progresses, and might result in changes in treatment plans.
One other example is a newborn patient in extreme pain following a surgical operation, who cannot convey their degree of suffering. Doctors may use EDA recordings and infer brain activity to evaluate how much pain the infant patient is in and intervene as needed.
For Faghih, this work could represent a breakthrough for mental health care. Monitoring the mental status of vulnerable people could help them get simpler care and stop severe consequences from declining mental health or swings in mood.
Her team is now working on ways to include the model into wearables, including the elimination of informational “noise” brought on by aspects like robust movement and exercise, in addition to searching for potential partnerships to design and manufacture the devices that will carry the algorithm.
This research was funded by a National Science Foundation CAREER award.
Source:
NYU Tandon School of Engineering
Journal reference:
Amin, R & Faghih, R.T., (2022) Physiological Characterization of Electrodermal Activity Enables Scalable Near Real-Time Autonomic Nervous System Activation Inference. PLOS Computational Biology. doi.org/10.1371/journal.pcbi.1010275.