Repairing Artifacts in Neural Activity Recordings Using Low-Rank Matrix Estimation.

ABSTRACT: Electrophysiology recordings are frequently affected by artifacts (e.g., subject motion or eye movements), which reduces the number of available trials and affects the statistical power. When artifacts are unavoidable and data are scarce, signal reconstruction algorithms that allow for the retention of sufficient trials become crucial. Here, we present one such algorithm that makes use of large spatiotemporal correlations in neural signals and solves the low-rank matrix completion problem, to fix artifactual entries. The method uses a gradient descent algorithm in lower dimensions to learn the missing entries and provide faithful reconstruction of signals. We carried out numerical simulations to benchmark the method and estimate optimal hyperparameters for actual EEG data. The fidelity of

SUBMITTER: Naik S 

PROVIDER: S-EPMC10220667 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature