Title :
Multireference adaptive noise canceling applied to the EEG
Author :
James, Christopher J. ; Hagan, Martin T. ; Jones, Richard D. ; Bones, Philip J. ; Carroll, Grant J.
Author_Institution :
Dept. of Electr. & Electron. Eng., Canterbury Univ., Christchurch, New Zealand
Abstract :
The technique of multireference adaptive noise canceling (MRANC) is applied to enhance transient nonstationarities in the electroencephalogram (EEG), with the adaptation implemented by means of a multilayer perceptron artificial neural network (ANN). The method was applied to recorded EEG segments and the performance on documented nonstationarities recorded. The results show that the neural network (nonlinear) gives an improvement in performance (i.e., signal-to-noise ratio (SNR) of the nonstationarities) compared to a linear implementation of MRANC. In both cases an improvement in the SNR was obtained. The advantage of the spatial filtering aspect of MRANC is highlighted when the performance of MRANC is compared to that of the inverse auto-regressive filtering of the EEG, a purely temporal filter.
Keywords :
adaptive signal processing; electroencephalography; medical signal processing; multilayer perceptrons; noise; nonlinear filters; electrodiagnostics; inverse autoregressive filtering; multilayer perceptron artificial neural network; multireference adaptive noise canceling; nonlinear neural network; purely temporal filter; signal-to-noise ratio; spatial filtering; transient nonstationarities enhancement; Adaptive filters; Artificial neural networks; Biomedical engineering; Bones; Brain modeling; Electroencephalography; Filtering; Hospitals; Noise cancellation; Nonlinear filters; Algorithms; Electroencephalography; Epilepsy; Humans; Linear Models; Neural Networks (Computer); Nonlinear Dynamics; Signal Processing, Computer-Assisted;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
