Sparsity-based Correction of Exponential Artifacts
This addresses artifact correction in biomedical recordings such as neural data, but it appears incremental as it builds on existing sparsity-based methods with modifications for specific artifact types.
The paper tackles the problem of removing exponential and piecewise smooth artifacts from biomedical time series like EEG and ECoG data by proposing an exponential transient excision algorithm (ETEA), formulated as a convex optimization problem with smoothed l1-norm regularization, and demonstrates its application on synthetic and real data examples.
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.