Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
A function estimation approach to sequential learning with neural networks
Neural Computation
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Adaptive filtering with the self-organizing map: a performance comparison
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Computational Intelligence and Neuroscience - Brain-Computer Interfaces: Towards Practical Implementations and Potential Applications
Dynamic fuzzy neural networks-a novel approach to functionapproximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Self-organizing topological tree for online vector quantization and data clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
Estimations of error bounds for neural-network function approximators
IEEE Transactions on Neural Networks
Approximation of nonlinear systems with radial basis function neural networks
IEEE Transactions on Neural Networks
RBF neural network center selection based on Fisher ratio class separability measure
IEEE Transactions on Neural Networks
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
Self-Organizing and Self-Evolving Neurons: A New Neural Network for Optimization
IEEE Transactions on Neural Networks
Higher-Order-Statistics-Based Radial Basis Function Networks for Signal Enhancement
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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Frequent occurrence of ocular artefacts leads to serious problems in reading and analysing the electroencephalogram (EEG) signal. These artefacts have high amplitude and overlapping frequency band with the physiological signal or real brain signal. Hence, it is difficult to reduce this type of artefacts by traditional filtering methods. In this paper, a novel ocular artefact removal method using artificial neural networks is described. 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 recursive least square error estimator techniques, the proposed system is suitable for real EEG applications. The advantages of the proposed method are demonstrated on EEG recordings by comparing with systems based on ICA. Our results demonstrate that this new system is preferable to other methods for ocular artefact reduction, achieving a better trade-off between removing artefacts and preserving inherent brain activities.