Abstract
Scalp recordings of cortical activations, Electroencephalography (EEG), are commonly used clinically to detect diseases or injuries to the underlying cortical physiology. Unfortunately, the EEG signal is also artifact prone and these artifacts can exhibit a similar temporal and spectral profile as that caused by the potential disease. We have created a model of simulated (synthetic) EEG and artifacts to explore their interplay and the theoretical limits of detection when artifacts may not be separable from clinical events of interest. A theoretical limit of separation without an EEG signal is derived and then simulated upper bounds for time-domain event detection are created using simulated EEG data.
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