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EEG discrimination using wavelet packet transform and a reduced-dimensional recurrent neural network
Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine
This paper proposes a novel reduced-dimensional recurrent neural network (NN) for electroencephalography (EEG) discrimination. Due to time-varying characteristics of EEG signals, recurrent NN is a useful approach for EEG pattern discrimination. However, when dealing with highdimensional data, NNs usually have problems of heavy computation burden and difficulty in training. To overcome these problems, the proposed NN incorporates a dimension-reducing stage into the network structure of adoi:10.1109/itab.2010.5687668 fatcat:hnyfgd32fba7ziccu6tx5au5gi