Signal processing of EEG for detection and classification ofclinical brain states

measures the brain activity via electrodes placed on the scalp, or in the case of evaluation for epilepsy brain

surgery, intracranially. The EEG measures very complex time varying signals from multiple and spatially

complicated sources. Clinically, EEG interpretation relies on visual interpretation of signals less than 30 Hz,

ignoring the rich motherlode of information from higher frequencies. Advanced signal processing is required to

make the EEG more useful for assessing clinical states. This proposal applies advanced signal processing to: 1)

assess the levels of anesthesia in patients undergoing surgery, 2) study the neurodynamics of focal seizure

activity measured both with intracranial and extracranial EEG electrodes with a view to understanding the

features and location of most active seizure activity, and 3) extract gamma frequency from the scalp EEG from

controls and patients so as to develop a biomarker for disease states such as dementia and schizophrenia. All of

these projects clearly have significant clinical applicability and commercial potential.

Faculty Supervisor:

Taufik Valiante

Student:

Partner:

Neurochip

Discipline:

Engineering

Sector:

Technology

University:

University of Toronto

Program:

Accelerate

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Government

Mitacs Partners