Non invasive brain stimulation is a widely used technique for several applications, generally aimed at modulating brain activity and thus behavior. While the behavioral effects can be monitored during the application of the stimulation, the electrophysiological correlates, such as electroencephalography (EEG), cannot, because the stimulation artifact dramatically affects the recorded signals. Here we addressed this problem and we analyzed the artifact that transcranial alternating current stimulation (tACS) leaves on EEG traces. We found that the stimulation noise adds itself non-linearly to the EEG signal and spectral analysis revealed a peak centered at the stimulation frequency. We then created a synthetic dataset by adding to real EEG traces numerically generated signals, matching the characteristics of the tACS artifact. We used this data to test a set of artifact removal techniques based on blind source separation (BSS) methods and wavelet decomposition and we found that the best performing technique is independent component analysis (ICA).