A quantitative method of analyzing EEG signals after stroke onset can help monitor disease progression and tailor treatments. In this work, we present an EEG-based imaging algorithm to estimate the location and size of the stroke infarct core and penumbra tissues. Building on recent advancements in localizing neural silences, we develop an algorithm that utilizes known spectral properties of the infarct core and penumbra to separately localize them. Our algorithm uses these properties to estimate source contributions to the scalp EEG recordings in different frequency bands. Subsequently, it utilizes optimization techniques to search for the affected brain sources iteratively. We test our algorithm on simulated datasets using a realistic MRI head model, achieving center-of-mass error of 12.80mm and 17.24mm, and size estimation error of 21.78% and 36.62% for infarct core and penumbra respectively.