title: The removal of EMG in EEG by neural networks.
abstract: In this paper, it is presented that electromyography (EMG) is a shot noise based on the generation of EMG. A novel filter is proposed by applying a neural network (NN) ensemble where the noisy input signal and the desired one are the same in a learning process. Both incremental and batch mode are applied in the learning process of NNs that is better than generalized NN filters. This NN ensemble filter not only reduces additive and multiplicative white noise inside signals, but also preserves the signals' characteristics. In clinical EEG and EMG signals processing, the filter is capable of reducing EMG in the clinical EEG, and it is proved that there is randomness in EMG.
authors.affiliation: Graduate School of Advanced Technology and Science, The University of Tokushima, Tokushima, Japan. cyj622@ee.tokushima-u.ac.jp
authors.forename: Yongjian
authors.initials: Y
authors.lastname: Chen
authors.forename: Masatake
authors.initials: M
authors.lastname: Akutagawa
authors.forename: Takahiro
authors.initials: T
authors.lastname: Emoto
authors.forename: Yohsuke
authors.initials: Y
authors.lastname: Kinouchi
mesh_terms: D000465:Algorithms; D016477:Artifacts; D004569:Electroencephalography / Q000295:instrumentation*; D004576:Electromyography; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D055815:Young Adult