Title :
Robust noise suppression techniques for neural signals
Author :
Lansford, James L. ; Kennedy, Philip R. ; Schroeder, James E.
Author_Institution :
Georgia Tech. Res. Inst., Atlanta, GA, USA
Abstract :
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
Keywords :
neurophysiology; noise; physiological models; signal processing; Laplace distributed error; algorithm; impulsive data extraction method; least-absolute-value model; least-squares model; neural activity spikes; neural signals; outliers; p-normed error models; scalar IRLS algorithm; Biological system modeling; Central nervous system; Data mining; Electrodes; Frequency; Glass; Least squares methods; Noise robustness; Signal processing; Wire;
Conference_Titel :
Engineering in Medicine and Biology Society, 1989. Images of the Twenty-First Century., Proceedings of the Annual International Conference of the IEEE Engineering in
Conference_Location :
Seattle, WA
DOI :
10.1109/IEMBS.1989.95929
