Could artificial intelligence help solve the mental health crisis? What if an algorithm allowed neurologists to know the area affected by a brain seizure?
These are just a few of the questions that Maryam Ravan, Ph.D., assistant professor of electrical and computer engineering at New York Institute of Technology, aims to address in new research studies published this fall. The studies, which have been featured in journals and conferences of the Institute of Electrical and Electronics Engineers (IEEE), propose cutting-edge algorithms and techniques that could provide non-invasive solutions for clinicians to more effectively treat neurological and psychiatric conditions.
Normal brain activity, including mood and thinking processes, relies on a balance of fast and slow brain waves. Imbalanced brain wave activity is associated with neurological issues, such as epilepsy, and psychiatric conditions like bipolar disorder and major depressive disorder.
One technique that clinicians use to detect brain wave imbalances is electroencephalography (EEG). This diagnostic test uses sensors to measure the electrical activity on a patient’s scalp, which is representative of the brain wave activity taking place underneath. Given this, brain wave data collected through EEG holds valuable information regarding an individual’s neurological and mental health.
Now, Ravan has partnered with physicians from prestigious institutions, including Stanford University and McMaster University (Canada), to propose new, non-invasive diagnostic techniques—founded on algorithms and engineering principles—that could improve how physicians use EEG brain wave data to treat the brain.
Locating the Source of a Seizure
Approximately three million adults and 470,000 children in the United States have epilepsy, a neurological condition that causes seizures. During a seizure, the brain’s normal electrical pattern is disrupted and sudden bursts of electrical energy can affect consciousness, movements, and sensations.
