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1. It is defined here in terms of cumulants, itself a concept whose WP article doesn't promise clarity. (If I were interested in cumulants, perhaps it would be more useful, I can't say. But that's not my current interest.)
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2. If I understand correctly, the bispectrum of 2 signals (f,g) is, precisely, cov(f+g,f*g), where "*" = convolution and "cov" = covariance (i.e., correlation x product of standard deviations).
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If so, it would be much more helpful to define it that way before explaining its relation to cumulants and its computation by
Fourier transform.
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I'm not sure what the bispectrum is, but the explanation here is poorly written.
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it encodes information that, say, EEG analysts have found to be meaningful, or
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one might turn to bispectra when studying certain kinds of related signals.
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