Woller, J. P. | Search bioRxiv

bioRxiv2024

Real-Time Spatiotemporal Filtering for Artifact-Free EEG during Electrical Neurostimulation

AO_SCPLOWBSTRACTC_SCPLOWCombining electrical neurostimulation with electroencephalography (EEG) for adaptive neurostimulation remains challenging due to the presence of stimulation artifacts in the recorded signal. Interpretation of EEG activity concurrent with stimulation requires real-time filtering of this noisy signal. While traditional frequency domain filters can suppress activity within frequency bands, they fail to differentiate sources in situations when there is a shared frequency characteristic between brain activity and stimulation signal. Here we present a new real-time denoising approach that combines spatial filtering and dynamic filter application. The spatiotemporal filter can suppress stimulation artifacts that share frequency bands with the brain signal in real-time while preserving the full spectral power. The spatial filter is also dynamically updated to account for possible changes in artifact topography. Finally, the filter is robust to changes of stimulation intensity, frequency and duration, enabling reliable denoising performance for inclusion in brain state-based closed-loop stimulation.

neuroscience↗

bioRxiv2024

EEG denoising during transcutaneous auricular vagus nerve stimulation across simulated, phantom and human data

ObjectiveThe acquisition of electroencephalogram (EEG) data during neurostimulation, particularly concurrent transcutaneous electrical stimulation of the auricular vagus nerve, introduces unique challenges for data preprocessing and analysis due to the presence of significant stimulation artifacts. This study evaluates various denoising techniques to address these challenges effectively. MethodsA variety of denoising techniques were investigated, including interpolation methods, spectral filtering, and spatial filtering techniques. The techniques evaluated included low-pass and notch filtering, spectrum interpolation, average artifact subtraction, the Zapline algorithm, and advanced methods such as independent component analysis (ICA), signal-space projection (SSP), and generalized eigendecomposition with stimulation artifact source separation (GED/SASS). The efficacy of these algorithms was evaluated across three distinct datasets: simulated data, data from a gelatin phantom model, and real human subject data. ResultsOur findings indicate that GED (SASS) and SSP significantly outperformed other methods in reducing artifacts while preserving the integrity of the EEG signal. ICA and Zapline were effective too, but came with important limitations. These methods demonstrated robustness across different data types and conditions, providing effective artifact mitigation with minimal disruption to other essential signal components. ConclusionThis comprehensive analysis demonstrates the efficacy of advanced spatial filtering techniques in the preprocessing of EEG data during auricular vagus nerve stimulation. These methods offer promising avenues for enhancing the quality and reliability of neurostimulation-associated EEG data, facilitating a deeper understanding and wider applications in clinical and research settings.

neuroscience↗