HI,
I have been using CRP tool box for over a month. I would like to have more references on when to use what type of method to find neighbors.
For instance there is max norm euclidean min norm rr fan etc. Given the source of data ( ex EEG, Population of a species over a time, gamma radiation from earth, daily temperature )
EEG is riddled with noise, gamma radiation is almost a constant except for few fluctuations, population data can be considered almost accurate, daily temperature may show seasonal variation and effects of global warming.
If one uses different threshold one gets a different plot. So how do we make predictions.
Thanks in advance.
Kindly excuse me if it is too basic or too advanced question.
Best regards
Sudharsana V I
Method of finding neighbours.
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- Junior
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- Affiliation (Univ., Inst., Dept.): University of Muenster
- Location: Muenster, Germany
- Research field: fMRI, EEG research, resting-state
Re: Method of finding neighbours.
Hi,
there is a nice review paper by Marwan et al. which explains the different types of norms: Marwan, N., Carmenromano, M., Thiel, M., & Kurths, J. (2007). Recurrence plots for the analysis of complex systems. Physics Reports, 438(5-6), 237–329. doi:10.1016/j.physrep.2006.11.001
Regarding RP and EEG there is some research e.g. by Marwan, Schinkel etc. which indicates that the Order Pattern RP is a good choice, since it is robust against noise and non-stationarity, see the following list of publications for example:
Schinkel, S., Marwan, N., & Kurths, J. (2007). Order patterns recurrence plots in the analysis of ERP data. Cognitive Neurodynamics, 1(4), 317–325. doi:10.1007/s11571-007-9023-z
Schinkel, S., Marwan, N., & Kurths, J. (2009). Brain signal analysis based on recurrences. Journal of Physiology Paris, 103(6), 315–323. doi:10.1016/j.jphysparis.2009.05.007
Schinkel, S., Zamora-López, G., Dimigen, O., Sommer, W., & Kurths, J. (2012). Order Patterns Networks (ORPAN)-a method to estimate time-evolving functional connectivity from multivariate time series. Frontiers in Computational Neuroscience, 6(November), 91. doi:10.3389/fncom.2012.00091
greetings
David
there is a nice review paper by Marwan et al. which explains the different types of norms: Marwan, N., Carmenromano, M., Thiel, M., & Kurths, J. (2007). Recurrence plots for the analysis of complex systems. Physics Reports, 438(5-6), 237–329. doi:10.1016/j.physrep.2006.11.001
Regarding RP and EEG there is some research e.g. by Marwan, Schinkel etc. which indicates that the Order Pattern RP is a good choice, since it is robust against noise and non-stationarity, see the following list of publications for example:
Schinkel, S., Marwan, N., & Kurths, J. (2007). Order patterns recurrence plots in the analysis of ERP data. Cognitive Neurodynamics, 1(4), 317–325. doi:10.1007/s11571-007-9023-z
Schinkel, S., Marwan, N., & Kurths, J. (2009). Brain signal analysis based on recurrences. Journal of Physiology Paris, 103(6), 315–323. doi:10.1016/j.jphysparis.2009.05.007
Schinkel, S., Zamora-López, G., Dimigen, O., Sommer, W., & Kurths, J. (2012). Order Patterns Networks (ORPAN)-a method to estimate time-evolving functional connectivity from multivariate time series. Frontiers in Computational Neuroscience, 6(November), 91. doi:10.3389/fncom.2012.00091
greetings
David