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Random matrix theory analysis of EEG data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lei, Min | - |
dc.contributor.author | Li, Jingdong | - |
dc.date.accessioned | 2019-05-14T03:04:57Z | - |
dc.date.available | 2019-05-14T03:04:57Z | - |
dc.date.issued | 2019-05-26 | - |
dc.identifier.citation | 13th International Conference on Applications of Statistics and Probability in Civil Engineering(ICASP13), Seoul, South Korea, May 26-30, 2019 | - |
dc.identifier.isbn | 979-11-967125-0-1 | - |
dc.identifier.other | ICASP13-210 | - |
dc.identifier.uri | https://hdl.handle.net/10371/153398 | - |
dc.description.abstract | In this study, we first construct the empirical cross-correlation matrices in the EEG signals by the Pearsons correlation coefficient. Then, we apply random matrix theory (RMT) investigate the statistical properties of cross-correlations in EEG data. The EEG signals of Alzheimer's disease (AD) subjects are chosen as the research objects. Here, we examine the statistical properties of cross-correlation coefficients, the distribution of eigenvalues. Meanwhile, we identify the deterministic or stochastic dynamics of the EEG system based on RMT. The correlation matrix of a Wishart matrix or a Gaussian Wigner matrix in random matrix theory has a structure similar to that of EEG data. For a noisy signal, its eigenvalue statistics are closely those of random matrix ensembles. The results show that the EEG signals are different from the stochastic time series. | - |
dc.language.iso | en | - |
dc.title | Random matrix theory analysis of EEG data | - |
dc.type | Conference Paper | - |
dc.identifier.doi | 10.22725/ICASP13.210 | - |
dc.sortNo | 790 | - |
dc.citation.pages | 1136-1139 | - |
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