Removal of Transcranial Alternating Current Stimulation EEG Artifacts Using Blind Source Separation and Wavelets.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Transcranial alternating current stimulation (tACS) modulates brain activity for cognitive and motor enhancement.
• Analyzing electroencephalography (EEG) during tACS is challenging due to large stimulation artifacts.
• Current limitations hinder the development of closed-loop tACS systems.

Purpose of the Study:

• To develop an effective method for removing tACS-induced artifacts from EEG data.
• To improve the analysis of tACS-EEG recordings for better understanding of tACS effects.
• To facilitate the implementation of real-time closed-loop tACS strategies.

Main Methods:

• Investigated blind source separation (BSS) techniques.
• Combined BSS methods with empirical wavelet transform (EWT) for artifact removal.
• Evaluated performance on simulated and experimental tACS-EEG data.

Main Results:

• The combination of EWT and BSS significantly outperformed BSS alone in artifact removal.
• Independent Vector Analysis (IVA) combined with EWT demonstrated the highest performance.
• The proposed method effectively denoised both simulated and experimental tACS-EEG data.

Conclusions:

• The novel EWT-BSS approach offers improved artifact removal for tACS-EEG analysis.
• This method holds promise for quantifying tACS effects and identifying biomarkers.
• It supports the development of real-time closed-loop tACS for brain activity modulation.