| 摘要(英) |
In this study, we take two electrophysiological signals obtained from organisms as examples, such as EEG, local field potential, blood pressure, etc. When we investigate Granger causality, it may be distorted by experimental error from input data. Then it will affect the result of Granger causality. Accordingly, we use numerical simulation to inquire four interfering factors: Time shift, phase shift, amplitude expansion or contraction and noise level. By this way, we will know how much impact will be on the values of the original Granger causality index when those factors occur on the high-frequency signal.
The results show that, we can find four interfering factors on the causality between the low-frequency signal x and the high-frequency signal y from the single factor simulations. The influence level from the least to the worst is amplitude expansion or contraction, time shift, phase shift, and noise level. In other words, we find the performance of amplitude expansion or contraction is more robust than other three interfering factors, because its impact on GCI is minimal. On the other hand, noise level has maximal impact on GCI. Then, by two-factor simulations, when we fix the factor of time shift, we can observe the GCI decline curve decreasing faster than other three factors. It also shows that the noise level has the smallest tolerance of input errors. If the strength of noise level is larger, it is enough to affect the signal waveform and impact to interpret the result of Granger causality.
Finally, we construct a mathematical model and the estimation formula of standard deviation of four interfering factors. According to the results of the simulation, we give the range of standard deviations of four interfering factors for the readers’ reference. |
| 參考文獻 |
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