Objective.We propose logLIRA, a novel method for the rejection of intracortical microstimulation artifacts in electrophysiological recordings specialized in the recovery of short-latency evoked activity. Additionally, we introduce a comprehensive comparison framework to evaluate the performance of logLIRA against previously reported algorithms. Approach.Our method estimates the artifact profiles by means of a piece-wise linear interpolation between logarithmically distributed points. It handles signal saturation thanks to a dynamically adjusted blanking interval. Finally, it deals with residual secondary artifacts by clustering and common activity rejection. The artifact rejection proficiency of logLIRA is evaluated against other state-of-the-art algorithms by means of a semisynthetic dataset acting as ground truth and enabling the computation of key performance metrics. Main results.The benchmark analysis highlights that our new method outperforms its competitors, enabling a robust recovery of short-latency evoked activity and, at the same time, minimizing the likelihood of introducing false positives as a consequence of misclassified residual artifacts. The results hold for very heterogeneous artifact profiles (with or without signal saturation) and across different combinations of mean artifacts rate and mean firing rate in the semisynthetic dataset. Additionally, we show the functioning of logLIRA with real-world data, where its capability to handle secondary artifacts and avoid inflated evoked responses is particularly evident. Significance.This work provides the scientific community with a valuable and powerful tool to improve the recovery of evoked responses, potentially contributing to a better understanding of functional connectivity and brain plasticity mechanisms inin vivoneural circuits as well as the advancement of therapeutic electrical neuromodulation techniques.
Negri et al. (Tue,) studied this question.