NYU Tandon researchers have reached a key milestone in their quest to develop wearable technology that measures key brain mechanisms through the skin.
faghi rose, Associate Professor of Biomedical Engineering, has been working for the past seven years on a technology that can measure mental activity through electrodermal activity (EDA), an electrical phenomenon of the skin that is influenced by brain activity related to emotional state . Internal stresses, whether caused by pain, exhaustion, or a particularly tight schedule, can cause changes in EDA, changes that are directly related to mental states.
The overall goal, a noninvasive intelligent multimodal brain state decoder for closed-loop adaptive wearable architectures, or MINDWATCH, as Faghih calls it, would act as a way to monitor a user’s state of mind and offer nudges that help them return to a state of mind. more neutral mood. For example, if a person was experiencing a particularly severe episode of work-related stress, MINDWATCH could detect this and automatically play relaxing music.
Now Faghih, along with Rafiul Amin, his former doctoral student, has performed a crucial task required to monitor this information. For the first time, they have developed a novel inference engine that can monitor brain activity through the skin in real time with high scalability and precision. The results are presented in a new article, “Physiological characterization of electrodermal activity enables scalable inference of autonomic nervous system activation in near real time”, published in PLOS Computational Biology.
“Inferring autonomic nervous system activation from wearable devices in real time opens up new opportunities to monitor and improve mental health and cognitive engagement,” according to Faghih.
Previous methods that measured sympathetic nervous system activation through the skin took minutes, which is impractical for wearable devices. While his previous work focused on inferring brain activity through the activation of sweat and other factors, the new study also models the sweat glands themselves. The model includes a 3D state space representation of direct sweat secretion through pore opening, as well as diffusion followed by corresponding evaporation and reabsorption. This detailed model of the glands provides exceptional insight for inferring brain activity.
The new model was run on data from 26 healthy individuals. The researchers showed that they can decipher brain signals with high reliability. Furthermore, the computational power requirement of their new algorithm is minimal and it can obtain brain and physiological information in a few seconds, while another previous approach would take minutes. This means that small, portable monitoring technology capable of incredible speed, high scalability, and extraordinary reliability is right at your fingertips.
The broader impact and applications of the methodology include performance monitoring, mental health monitoring, pain measurement, and cognitive stress. Mental health tracking can help better manage autism, post-traumatic stress disorder, excessive irritability, suicidality, and more. Performance tracking and cognitive stress monitoring can help improve individual productivity and quality of life.
“One’s performance changes based on one’s cognitive engagement and arousal levels.” Faghih says. For example, very low or very high levels of arousal can result in poor performance. Therefore, it is expected that. Ultimately, researchers can use the inferred autonomic nervous system activation and decoded arousal to develop interventions to improve productivity.”
An example of the application of this method is the early diagnosis of disorders such as diabetic neuropathy. Small nerves transmit brain stimulation to many parts of the body, including those involved in the skin’s conductance response. To track received brain activity, EDA can be measured and monitored on a regular basis in areas of the body’s skin that are prone to neuropathy. If an area of the skin has neuropathy (that is, small nerves have been damaged), the brain will not activate that area. By monitoring changes, doctors can see how a condition like diabetic neuropathy is progressing and can lead to changes in treatment plans.
Another example is a newborn patient with extreme pain after a surgical procedure, who cannot convey his degree of suffering. Clinicians can use EDA recordings and infer brain activity to assess how much pain the child 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 more effective care and avoid serious consequences of deteriorating mental health or mood swings.
His team is now working on ways to incorporate the model into wearable devices, including removing informational “noise” caused by factors such as intense movement and exercise, as well as looking at potential partnerships to design and manufacture the devices that would carry the algorithm.
Reference: Amin R, Faghih RT. Physiological characterization of electrodermal activity enables scalable inference of autonomic nervous system activation in near real time. PLOS biocomputing. 2022;18(7):e1010275. do: 10.1371/diary.pcbi.1010275
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