Brain Monitoring and Modulation

During Phase 2, EpLink began a partnership with industry experts at Avertus Inc. to further innovation and commercial opportunities that can directly benefit the epilepsy community. Engineers at Avertus developed a wireless headset that a person with epilepsy can wear comfortably to monitor their brain waves using electroencephalography (EEG). This information is then sent directly to a computer or mobile device, where it can be reviewed by physicians and can also provide seizure warnings whenever needed. The headset is now being sold to researchers and will soon be approved for testing on patients with epilepsy. You can find more information about Avertus’ headset at https://www.avertus.ca/.

Drs. Berj Bardakjian and Carlen are developing and testing algorithms using artificial intelligence for scalp EEG identification of seizure onset state (warning) and occurrence with plans to combine with an EEG headset system. Also, using this technology, they are part of a competition to identify biomarkers for Sudden Unexpected Death in Epilepsy (SUDEP) from the scalp EEG.

Dr. Liang Zhang has developed a unique mouse model of recurrent seizures most useful for the testing of seizure treatment strategies, including pharmacotherapy and brain stimulation paradigms.

Drs. Taufik Valiante, Jose Luis Perez-Velazquez and Roman Genov developed a method to anticipate seizures and to stop seizure generation using intracerebral electrical brain stimulation. The team developed a novel computer chip (Neural Interface Processor – or NURIP) that can be surgically placed within brains of people with epilepsy to deliver brain stimulation when a seizure is about to start. This method has been effective in stopping seizures in animals, and now, human studies have begun with hopes that it will have the same effect. Led by Dr. Valiante at Toronto Western Hospital, this study examines deep brain stimulation activity in patients with implanted intracranial electrodes at the epilepsy clinic. This study is ongoing and aims to develop machine learning approaches to detect when seizures start and deliver targeted stimulation to the brain to stop seizures from occurring.

Ultimately, results from these studies may help us understand how seizure activity develops and spreads, and may assist in the development of new technologies that decrease seizure frequency in people with living.