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The combination of transcranial alternating current stimulation (tACS) and electroencephalogram (EEG) for mobile and home-based interventions offers the potential for control and adaptation of stimulation parameters. Yet, during stimulation, the EEG is heavily affected by stimulation artifacts. Spatial filters are often unsuited because too few channels are recorded and hardware capabilities are limited. Due to their speed and as they can be used for single channels, we explore the performance of single-channel weighted comb filters on artifact removal. At any given time point t, the recording r(t) is a superposition of a neurophysiological signal n(t), the stimulation artifact a(t) and noise e(t). Now, we can estimate the artifact a(t) based on the recording from an earlier (or later) time-point shifted by the artifacts period. A weighted estimate based on multiple time points has the potential to improve the signal recovery. Therefore, we explored several approaches and evaluated their performance on simulated and real data. The comb kernel filters were implemented in Matlab (https://github.com/agricolab/ARtACS) and Python (https://github.com/agricolab/pyARtACS), and the code is open access under an X11-license. We found that independent of the weighting function, all comb filters exhibit similarity in their suppression of the DC component, the artifacts frequency, and its harmonics. Yet, different weighting functions exhibit different pass-band performance, evident as ringing and amplification, and their induction of time-domain echoes. Interestingly, we note that a causal uniform filter is comparable to more complex approaches, while offering the option for real-time filtering. Comb filters are able to remove tACS artifacts even if only a single channel is available. As comb filters require no assumptions about the shape of the artifact, they might also be useful for filtering of non-sinusoidal, e.g. pulsed or saw-tooth, transcranial current stimulation.

Comb filters for the removal of transcranial current stimulation artifacts from single channel EEG recordings