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An Improved Adaptive Filtering Approach For Removing Artifact From The Electroencephalogram, Justin Ronald Estepp

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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 …