Sparsity-based Correction of Exponential Artifacts

TL;DR

This paper introduces ETEA, a convex optimization-based algorithm for removing exponential and irregular artifacts from biomedical time series like EEG and ECoG, improving data quality for neural analysis.

Contribution

It proposes a novel convex optimization framework with a smoothed l1-norm regularizer for artifact correction in neural recordings, adaptable to different artifact types.

Findings

Effective removal of exponential transients demonstrated on synthetic and real data.

Enhanced suppression of ocular artifacts in EEG data.

Algorithm is computationally feasible for practical neural data analysis.

Abstract

This paper describes an exponential transient excision algorithm (ETEA). In biomedical time series analysis, e.g., in vivo neural recording and electrocorticography (ECoG), some measurement artifacts take the form of piecewise exponential transients. The proposed method is formulated as an unconstrained convex optimization problem, regularized by smoothed l1-norm penalty function, which can be solved by majorization-minimization (MM) method. With a slight modification of the regularizer, ETEA can also suppress more irregular piecewise smooth artifacts, especially, ocular artifacts (OA) in electroencephalog- raphy (EEG) data. Examples of synthetic signal, EEG data, and ECoG data are presented to illustrate the proposed algorithms.