Robust noise suppression techniques for neural signals
Jim Lansford, Philip R. Kennedy, Jim Schroeder · 1989
A method of extracting impulsive data using p-normed error models, where p=2 corresponds to the least-squares model and p=1 corresponds to the least-absolute-value case, is discussed. The least-absolute-value model is found to be best when the model error is Laplace distributed. Thus, a judicious choice of p-normed model allows outliers, such as the spikes from neural activity, to be passed through the algorithm while other types of noise are suppressed. Results obtained with the scalar IRLS algorithm are presented and discussed.>