LAUSR

Abstract Goal: 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. Methods: We examined the performance of several BSS methods both applied individually, as well as combined with the empirical wavelet transform (EWT) in terms of denoising realistic simulated and experimental tACS-EEG data. Results: EWT combined with BSS yielded considerably improved performance compared to BSS alone for both simulated and experimental data. Overall, independent vector analysis (IVA) combined with EWT yielded the best performance. Significance: The proposed method yields promise for quantifying the effects of tACS on simultaneously recorded EEG data, which can in turn contribute towards understanding the effects of tACS on brain activity, as well as extracting reliable biomarkers that may be used to develop closed-loop tACS strategies for modulating the underlying brain activity in real time.