Dynamic Fuzzy Neural Network Based Learning Algorithms for Ocular Artefact Reduction in EEG Recordings

Abstract

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.