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 growing ANN that optimized the number of nodes in the hidden layer and the coefficient matrices, which are optimized by the simultaneous perturbation method. The ANN improves the results obtained with the conventional EEG filtering techniques: wavelet, singular value decomposition, principal component analysis, adaptive filtering and independent components analysis. 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.

The electroencephalogram (EEG) signal is the manifestation of brain activity recorded as changes in electrical potentials at multiple locations over the scalp and it can be distorted by many other sources of electrical activity, called eye artefacts. It is important to remove these artefact signals before analysing the EEG signal, to obtain accurate information about brain activity and avoid mistakes in its interpretation. To deal with this problem, the present study proposes an artificial neural network, as a filter to remove ocular artefacts. In the proposed method, the number of radial basis function (RBF) neurons and input output space clustering are adaptively determined. Furthermore, the structure of the system and the parameters of the corresponding RBF units are trained automatically and relatively fast adaptation is attained. By the leastsquare error estimator techniques, the proposed system is suitable for real EEG applications. The proposed system improves results yielded by conventional techniques of ocular reduction, such as principal component analysis, support vector machines and independent component analysis systems. As a consequence, the algorithm could serve as an effective framework to reduce substantially eye interference in EEG recordings.

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