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Objective: We present a novel signal processing algorithm for automated, noninvasive detection of cortical spreading depolarizations (CSDs) using electroencephalography (EEG) signals and validate the algorithm on simulated EEG signals. CSDs are waves of neurochemical changes that suppress the neuronal activity as they propagate across the brain's cortical surface. CSDs are believed to mediate secondary brain damage after brain trauma and cerebrovascular diseases like stroke. We address the following two key challenges in detecting CSDs from EEG signals: i) attenuation and loss of high spatial resolution information; and ii) cortical folds, which complicate tracking CSD waves. Methods: Our algorithm detects and tracks “wavefronts” of a CSD wave, and stitch together data across space and time to make a detection. To test our algorithm, we provide different models of CSD waves, including different widths of CSD suppressions and different patterns, and use them to simulate scalp EEG signals using head models of four subjects. Results and conclusion: Our results suggest that low-density EEG grids (40 electrodes) can detect CSD widths of 1.1 cm on average, while higher density EEG grids (340 electrodes) can detect CSD patterns as thin as 0.43 cm (less than minimum widths reported in prior works), among which single-gyrus CSDs are the hardest to detect because of their small suppression area. Significance: The proposed algorithm is a first step toward noninvasive, automated detection of CSDs, which can help in reducing secondary brain damages. [ABSTRACT FROM AUTHOR]