An Adaptive Radial Basis Function Neural Network Filter for Noise Reduction in Biomedical Recordings

Electroencephalogram (EEG) recordings often experience interference by different kinds of noise, including white, muscle and baseline, severely limiting its utility. Artificial neural networks (ANNs) are effective and powerful tools for removing interference from EEGs. Several methods have been developed, but ANNs appear to be the most effective for reducing muscle and baseline contamination, especially when the contamination is greater in amplitude than the brain signal. An ANN as a filter for EEG recordings is proposed in this paper, developing a novel framework for investigating and comparing the relative performance of an ANN incorporating real EEG recordings. This method is based on a higher-order statistics-based radial basis function (RBF) network. This ANN improves the results obtained with the conventional EEG filtering techniques: wavelet, singular value decomposition, principal component analysis, adaptive filtering and independent components analysis. Average results for the RBF-based method provided a noise reduction (SIR) of (mean$$\pm $$± SD) $$\mathrm{SIR}=19.3\pm 0.3$$SIR=19.3±0.3 in contrast to traditional compared methods that, for the best case, yielded $$\mathrm{SIR}=15.2\pm 0.3$$SIR=15.2±0.3. The system has been evaluated within a wide range of EEG signals. The present study introduces a new method of reducing all EEG interference signals in one step with low EEG distortion and high noise reduction.