Transcranial alternating current stimulation (tACS) is a non-invasive technology for modulating brain activity, with significant potential for improving motor and cognitive functions. To investigate the effects of tACS, many studies have used electroencephalographic (EEG) data recorded during brain stimulation. However, the large artifacts induced by tACS make the analysis of tACS-EEG recordings challenging, which in turn has prevented the implementation of closed-loop brain stimulation schemes. Here, we propose a novel combination of blind source separation (BSS) and wavelets to achieve removal of tACS-EEG artifacts with improved performance.