How to compute a tri-coherence

In summary, the speaker is looking for a code or software to compute tricoherence from time-series data. They have not been able to find one and are considering writing their own code. The other person suggests using synthetic data as a benchmark and asks about the speaker's project, but the speaker is unable to share details at the moment.
  • #1
maxime.lesur
11
0
Dear all,

I have time-series data, from which I would like to compute the tricoherence. After some googling, it seems that I will have to write my own code. But before I begin, let me ask this.

Do anyone here know of a code, library (fortran, c), module (python, matlab, IDL, mathematica) or software to compute a tri-coherence?

Thank you!
 
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  • #2
Not just the bicoherence, but the tricoherence?
 
  • #3
Yes indeed, tri, not bi.
 
  • #4
I see. I don't know of such a package offhand, but this is specialized enough that you might want to code it yourself anyway, just to be sure what is going into your calculation.

Out of curiosity, what sort of problem are you working on (if you wouldn't mind sharing)?
 
  • #5
You're right, I'd better code it anyway. But a package would be helpful to benchmark against my own code.

Sorry, I can't share my project before we have some paper out. I'll try to answer to you in a few months.
 
  • #6
Ok. I suggest using processing synthetic data as the best benchmark. Well good luck!
 

Related to How to compute a tri-coherence

1. What is a tri-coherence and why is it important in scientific analysis?

A tri-coherence is a measure of the coherence between three signals or variables. It is important in scientific analysis because it allows for the identification of complex relationships between three variables, which may not be evident when looking at pairs of variables.

2. How is a tri-coherence computed?

A tri-coherence is computed by first calculating the cross-spectral density between the three signals. This is then divided by the product of the individual spectral densities. The resulting value ranges from 0 to 1, with higher values indicating a stronger coherence between the three variables.

3. What type of data is needed to compute a tri-coherence?

A tri-coherence can be computed using any type of data that can be represented as a time series, such as EEG, ECG, or fMRI data. However, the data should be evenly sampled and have sufficient length for meaningful analysis.

4. Can a tri-coherence be used to determine causality?

No, a tri-coherence cannot be used to determine causality between variables. It only measures the strength of coherence between three variables, but does not indicate the direction of the relationship.

5. Are there any limitations to using tri-coherence in scientific analysis?

Yes, there are some limitations to using tri-coherence. It assumes linearity and stationarity of the data, which may not always be the case. It is also sensitive to noise and requires careful interpretation, as high values may not necessarily indicate a meaningful relationship between variables.

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