Detecting quadratic phase coupling from time series data by rhosa

Takeshi Abe



In this vignette we show how to detect quadratic phase coupling (QPC) of one-dimensional or multi-dimensional real-valued time series by bicoherence or cross_bicoherence of rhosa, respectively. The first section gives an example applying bicoherence to the data from a simple model exhibiting QPC at each frequency pair. In the second section we describe a generative model of three channels with another kind of QPC revealed by cross_bicoherence. The third section summarizes when and why bicoherence or cross_bicoherence fails to recognize a certain type of QPC.

A toy model of QPC

We begin with importing rhosa:

#> Welcome to rhosa

Our first mathematical model, adapted from [1], is a superposition of six cosine curves of unit amplitude with different frequencies, named \(x_1\):

\[x_1(t) = \sum_{i=1}^{6} \cos(\lambda_i t + \varphi_i)\]

where, for each \(i = 1,2,...,6\), \(\lambda_i\) is given and fixed, namely

\[ \lambda_1 = 0.55; \lambda_2 = 0.75; \lambda_3 = \lambda_1 + \lambda_2; \] \[ \lambda_4 = 0.6; \lambda_5 = 0.8; \lambda_6 = \lambda_4 + \lambda_5. \]

On the other hand, we choose \(\varphi_i\) (\(i = 1, ..., 5\)) independently from the uniform variable of range \([0, 2\pi)\), and define

\[ \varphi_6 = \varphi_4 + \varphi_5. \]

Note that the trigonometric identities implies

\[\cos(\lambda_6 t + \varphi_6) = \cos((\lambda_4 + \lambda_5) t + (\varphi_4 + \varphi_5)) = \cos(\lambda_4 t + \varphi_4) \cos(\lambda_5 t + \varphi_5) - \sin(\lambda_4 t + \varphi_4) \sin(\lambda_5 t + \varphi_5),\]

so \(\cos(\lambda_6 t + \varphi_6)\) is positively correlated with the product of \(\cos(\lambda_4 t + \varphi_4)\) and \(\cos(\lambda_5 t + \varphi_5)\). But \(\cos(\lambda_3 t + \varphi_3)\) is not correlated with the product of \(\cos(\lambda_1 t + \varphi_1)\) and \(\cos(\lambda_2 t + \varphi_2)\) in general as the phase \(\varphi_3\) is randomly assigned.

Once \(\varphi_i\)s are chosen, \(x_1(t)\) is a periodic function of \(t\). So it turns out that \(x_1\) is a (strictly) stationary stochastic process. The wave length of any frequency component of \(x_1\) is shorter than \(4\pi\). Now consider sampling a realization of \(x_1\) repeatedly during a fixed length of time, say 256. The sampling rate is 1. That is, the interval of consecutive samples is 1. The following R code effectively simulates \(x_1\) as x1.

triple_lambda <- function(a, b) c(a, b, a + b)
lambda <- c(triple_lambda(0.55, 0.75), triple_lambda(0.6, 0.8))
x1 <- function(k) {
    init_phi <- runif(5, min = 0, max = 2*pi)
    phi <- c(init_phi, init_phi[4] + init_phi[5])
    function(t), Map(function(l, p) cos(l * t + p), lambda, phi))
observe <- function(f) {
    sapply(seq_len(256), f)
N1 <- 100
m1 <-, Map(observe, Map(x1, seq_len(N1))))

Each column of matrix m1 in the last line represents a realization of x1. That is, we have taken 100 realizations of \(x_1\) as m1. Let’s plot them:

ith_sample <- function(i) {
    data.frame(i = i, t = seq_len(256), v = m1[,i])
r1 <-, Map(ith_sample, seq_len(100)))


ggplot(r1) +
    geom_line(aes(t, v)) +
    facet_wrap(vars(i)) +
    theme(axis.title.y = element_blank(),
          axis.text.y = element_blank(),
          strip.background = element_blank(),
          strip.text.x = element_blank())

100 realizations of x1.

A realization of \(x_1\) looks like:

plot(m1[,1], type = "l", ylim = c(-5, 5), xlab = "t", ylab = "")

The 256 time points in the first realization of x1.

Its spectrum estimation shows peaks at six frequencies as expected:


The spectrum estimation of x1.

Now we approximate \(x_1\)’s bicoherence by bicoherence:

bc1 <- bicoherence(m1)

… and define an R function to plot the heat map of the estimated bicoherence:

heatmap_bicoherence <- function(bc) {
    ggplot(bc) +
        geom_point(aes(f1, f2, colour = value), alpha = 0.5, size = 1) +
        scale_alpha(guide = "none")


x1’s estimated bicoherence.

Note that the highest peak is at the bifrequency \((f_1, f_2) = (\frac{\lambda_5}{2 \pi}, \frac{\lambda_4}{2 \pi}) \approx (0.127, 0.095)\).

A three-channel model of quadratic signal processing

Another example of QPC consists of three channels, which accept series of periodic input signals and suffer from Gaussian noises. From a couple of the channels, say \(C_1\) and \(C_2\), we observe the summation of input and noises as their output. On the other hand the last channel, called \(C_3\), adds \(C_1\)’s output multiplied by \(C_2\)’s to its own input and noise.

The following block diagram shows the skeleton of our three-channel model.
The block diagram of the three-channel model.
The block diagram of the three-channel model.

Here, assume that \(C_1\)’s input is a triangle wave of a fixed frequency with varying initial phases. A rectangle wave of another frequency for \(C_2\)’s input. A cosinusoidal curve of yet another frequency for \(C_3\)’s input. Running the following code simulates the model.

Fcoef1 <- 1.2
Fcoef2 <- 0.7
Fcoef3 <- 0.8

i1 <- function(x, p) {2 * asin(sin(Fcoef1 * x + p))}
i2 <- function(x, p) {ifelse(cos(Fcoef2 * x + p) >= 0, -1, 1)}
i3 <- function(x, p) {cos(Fcoef3 * x + p)}

Qcoef <- 0.3

tc <- function(k) {
    ps <- runif(3, min = 0, max = 2*pi)
    function(x) {
        c1 <- i1(x, ps[1]) + rnorm(length(x), mean = 0, sd = 1)
        c2 <- i2(x, ps[2]) + rnorm(length(x), mean = 0, sd = 1)
        c3 <- Qcoef * c1 * c2 +
            i3(x, ps[3]) + rnorm(length(x), mean = 0, sd = 1)
        data.frame(c1, c2, c3)

N2 <- 100

sample_tc <- function() {
    Map(function(f) {f(seq_len(256))}, Map(tc, seq_len(N2)))

c1_data_frame <- function(y) {, Map(function(k) {y[[k]]$c1}, seq_len(N2)))

c2_data_frame <- function(y) {, Map(function(k) {y[[k]]$c2}, seq_len(N2)))

c3_data_frame <- function(y) {, Map(function(k) {y[[k]]$c3}, seq_len(N2)))

y1 <- sample_tc()
d1 <- c1_data_frame(y1)
d2 <- c2_data_frame(y1)
d3 <- c3_data_frame(y1)

That is, we obtain 100 series of data with 256 points. To be specific, \(C_1\)’s triangle wave has cycle \(\frac{2 \pi}{1.2}\), the cycle of \(C_2\)’s rectangle wave is \(\frac{2 \pi}{0.7}\), and the one of \(C_3\)’s cosinusoidal wave is \(\frac{2 \pi}{0.8}\).

\(C_1\)’s output looks like:

plot(d1[,1], type = "l", ylim = c(-5, 5), xlab = "t", ylab = "")

A sample path of d1.

And \(C_2\)’s output:

plot(d2[,1], type = "l", ylim = c(-5, 5), xlab = "t", ylab = "")

A sample path of d2.

\(C_3\)’s output:

plot(d3[,1], type = "l", ylim = c(-5, 5), xlab = "t", ylab = "")

A sample path of d3.

Their spectrum estimation show significant peaks at expected frequencies:


The spectrum estimation of C1.