THE WiNTER LAB
A WEARABLE EEG SYSTEM FOR PERSONALIZED EPILEPSY MANAGEMENT
In epilepsy and several other neurological disorders, extended monitoring and analysis of brain activity is vital for accurate diagnosis, tracking treatment effectiveness, and enabling timely, precise adjustments to meet patients’ evolving needs. Led by Hossein Kassiri (York University) and Gavin Winston (Queen’s University), this team is developing a smart, wearable electroencephalogram (EEG) device designed for clinical accuracy, long-term comfort, and ethical use in everyday environments.
Unlike traditional hospital-based EEGs, this custom-fitted cap will allow patients, beginning with those with epilepsy, to monitor brain activity at home without compromising data quality. The device integrates AI-powered chips to detect abnormalities and forecast seizures in real time, while accounting for diverse anatomical and hair-type differences. Through co-creation workshops with people with epilepsy and caregivers, the team ensures the technology meets real-world needs. Grounded in engineering, medicine, ethics, and social science, this project embodies Connected Minds’ commitment to responsible, patient-centered innovation.
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UNDERSTANDING COGNITIVE DEFICITS WITH ROBOTICS AND NEUROIMAGING
People with epilepsy often have cognitive problems including memory and executive function. Assessment with conventional neuropsychology is expensive and not readily available, so this disability is often undetected and not addressed.
We are using a robotic technology called Kinarm to detect such impairments more easily and at lower cost, and performing detailed brain scans to gain a better understanding of the underlying structural and functional changes that may help guide the development of future treatments.
MULTIMODAL NEUROIMAGING AND EPILEPSY SURGERY
Around 30% with epilepsy are medically refractory and fail to respond to anti-seizure medications. Epilepsy surgery offers up to an 80% chance of seizure freedom in appropriate chosen patients.
We are implementing neuroimaging techniques to identify the epileptogenic zone, assess language lateralization and delineate eloquent white matter pathways and integrating these into 3D models to improve clinical decision making in the planning of epilepsy surgery within the Kingston Health Sciences Centre District Epilepsy Centre.
PREDICTING RECURRENCE
RISK AFTER FIRST SEIZURE
Up to 10% of people experience a seizure in their life, and if no cause is identified this is called an unprovoked first seizure (UFS). Around 40-50% have further seizures, but current approaches do not allow us to accurately determine those at risk of further seizures.
In a multi-centre study collaborating with Nova Scotia Health Authority (PI Dr. Tonya Omisade), we are developing accurate methods for predicting seizure recurrence risk using machine learning approaches applied to cognitive, MRI and EEG data.
CURRENT FUNDERS
PREVIOUS FUNDERS
