Robust brain-computer interface (BCI) control during frequency-tuned transcranial alternating current stimulation (tACS)

This repository contains the original EEG dataset for the study "Robust brain-computer interface (BCI) control during frequency-tuned transcranial alternating current stimulation (tACS)". The manuscript is currently under revision. Abstract: Integrating frequency-tuned, adaptive brain stimulation with brain-computer interfaces (BCIs) allows direct tests of the causal role of brain oscillations and advances BCIs toward bi-directional operation. A central challenge is that stimulation-induced artifacts overlap with endogenous brain rhythms, undermining robust real-time signal decoding and contingent BCI feedback. Here, we overcome this limitation by introducing an artifact-suppression approach that enables robust motor-imagery BCI control during frequency-tuned amplitude-modulated transcranial alternating current stimulation (AM-tACS). We developed a real-time spatial filtering pipeline that combines spatio-spectral decomposition (SSD) with beamforming and evaluated its performance against a standard Laplacian filter in 14 healthy participants. BCI control was assessed both in the absence of stimulation and during stimulation. We hypothesized that only the SSD–beamforming approach would preserve robust BCI control under stimulation. In the absence of stimulation, both pipelines supported robust BCI control (SSD–beamforming: 73 ± 9%; Laplacian: 72 ± 8%; p = .849). During AM-tACS, Laplacian filtering showed a marked performance decline to near chance level (58 ± 9%; p < .001). However, with SSD–beamforming, robust BCI control was preserved (76 ± 9%). These results demonstrate that robust BCI control during frequency-tuned AM-tACS is achievable. By enabling simultaneous stimulation and decoding, this approach establishes a new paradigm for testing the causal contributions of brain rhythms during ongoing BCI control and for advancing stimulation-informed, bi-directional BCI interventions. Future work will determine how AM-tACS can be leveraged to enhance BCI performance but also promote neuroplasticity during restorative BCI applications.