Real-time change detection of steady-state evoked potentials

Abstract

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 alerts when changes in the SSEP are detected by applying a two-step Generalized 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 system's performance is analyzed using Monte Carlo simulations and tested on real EEG data recorded under coma.

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