A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN
Neural Computing and Applications - Special Issue - KES2008
A Functional-Link-Based Neurofuzzy Network for Nonlinear System Control
IEEE Transactions on Fuzzy Systems
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
Hi-index | 0.01 |
EEG signal is an important clinical tool for diagnosing, monitoring, and managing neurological disorders. This signal is often affected by a variety of large signal contaminations or artifacts, which reduce its clinical usefulness. In this paper, a new adaptive FLN-RBFN-based filter is proposed to cancel the three most serious contaminants, i.e. ocular, muscular and cardiac artifacts from EEG signal. The basic method used in this paper for the elimination of artifacts is adaptive noise cancellation (ANC). The results demonstrate the effectiveness of the proposed technique in extracting the desired EEG component from contaminated EEG signal.