|M.Sc Student||Gideon Nave|
|Subject||Real Time Change Detection of Steady-State Evoked|
|Department||Department of Electrical Engineering||Supervisors||Professor Emeritus Inbar Gideon (Deceased)|
|Professor Emeritus Pratt Hillel|
|Professor Zaaroor Menashe|
|Full Thesis text|
Evoked potentials (EP) are the electrical potential generated by the activity of neurons in the brain, and recorded from a human or animal following presentation of a stimulus. EP amplitudes are typically in the order of a microvolt, compared to tens or hundreds of microvolts for spontaneous EEG activity. EP from
the central nervous system consist of three major types according to the sensory system activated: Auditory, Visual and Somato-sensory evoked potentials.
Steady-state evoked potentials (SSEP) are the electrical activity recorded from the scalp in response to high-rate sensory stimulation. SSEP consist of a constituent frequency component matching the stimulation rate, whose amplitude and phase remain constant with time and are sensitive to functional changes in
the stimulated sensory system.
Monitoring SSEP during neurosurgical procedures allows identification of an emerging impairment early enough before the damage becomes permanent. In routine practice, SSEP are extracted by averaging of the EEG recordings, allowing detection of neurological changes within approximately a minute. As an alternative to the relatively slow-responding empirical averaging, we present an algorithm that detects changes in the SSEP within seconds. Our system alarms when changes in the SSEP are detected by applying a two step General Likelihood Ratio Test (GLRT) on the unaveraged EEG recordings. This approach, outperforms conventional detection and provides the monitor with a statistical measure of the likelihood that a change
occurred, thus enhancing its sensitivity and reliability. The algorithm’s performance is analyzed using Monte Carlo simulations and compared to previous SSEP monitoring methods, based on measures of Mean Time between False Alarms and Mean Time for Detection. The algorithm was tested on real EEG data recorded under coma at the Neurosurgery department in Rambam hospital, Haifa.