An Improved Adaptive Filtering Approach for Removing Artifact from the Electroencephalogram
Year: 2015
Type: article
Abstract: The biophysics of volume conduction that enable electrophysiological data acquisition also result in the mixing of data sources including possible, undesirable noise sources at the electrode interface. This work specifically focuses on improving the performance of the recursive least-squares (RLS) adaptive filtering method for removing eye movement artifact from the electroencephalogram. In biophysically-inspired simulated data, the RLS algorithm is verified to produce results that are inferior to extended infomax independent component analysis (ICA), the most widely used artifact correction approach in this problem space, due to its non-linear filter phase response and the presence of bidirectional contamination, or cross-talk, resultant of volume conduction in electroencephalographic data. The non-linear phase response of the RLS algorithm is mitigated by restricting its filter coefficients to form a linear phase, Type I finite impulse response filter. A reduced effect of cross-talk in RLS is achieved by filtering the reference noise input signal using a combination of non-local means weighting and Bayesian adaptive regression splines smoothing. When compared to extended infomax ICA, the modified RLS adaptive filtering approach meets or exceeds data source recovery accuracy while retaining highly desirable properties not afforded by blind source separation. These results support the use of a modified adaptive filtering approach for the near-ideal removal of eye artifact data from the electroencephalogram.
Author: Justin R. Estepp
Institution: Wright State University
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2506007367