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In this paper, we propose a method to analyze epileptic electroencephalogram based on time series that is transformed from improved k-nearest neighbor network. The study of complex networks has become a hot research of electroencephalogram signal. Electroencephalogram time series generated by the network keeps node information of network, so researching the time series from the network can also achieve...
Multivariate multiscale entropy (mvMSE) has been proposed as a combination of the coarse-graining process and multivariate sample entropy (mvSE) to quantify the irregularity of multivariate signals. However, both the coarse-graining process and mvSE may not be reliable for short signals. Although the coarse-graining process can be replaced with multivariate empirical mode decomposition (MEMD), the...
A brain computer interface (BCI) is a novel communication system that translates brain signals into control commands. In this paper, we present a P300 BCI system based on ordinal pattern features. Compared to BCI system based on linear time domain features, we have shown that slightly better classification accuracies and bitrates can be achieved for healthy and disabled subjects.
The nonlinearity in normal and epileptic electroencephalogram (EEG) signals is investigated in this paper by the delay vector variance (DVV) method, which determines the degree of nonlinearity of the tested time series by comparing the target variances of the tested time series to those of the corresponding surrogate time series. The results of numerical experiments show that both normal and epileptic...
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