Neural network based EEG denoising.
A novel filter is proposed by applying back propagation neural network (BPNN) ensemble where the noisy signal and the reference one are the same in a learning process. This neural network (NN) ensemble filter not only well reduces additive and multiplicative white noise inside signals, but also preserves signals' characteristics. It is proved that the reduction of noise using NN ensemble filter is better than the improved epsilon nonlinear filter and single NN filter while signal to noise ratio is smaller. The performance of the NN ensemble filter is demonstrated in computer simulations and actual electroencephalogram (EEG) signals processing.
Chen
Yongjian
Y
Graduate School of Advanced Technology and Science, The University of Tokushima, Japan. cyj622@ee.tokushima-u.ac.jp
Akutagawa
Masatake
M
Katayama
Masato
M
Zhang
Qinyu
Q
Kinouchi
Yohsuke
Y
D000465:Algorithms; D016477:Artifacts; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity