Algorithm 1 Microstate Network Model

Step 1: According to the microstate template, the microstates A, B, C, and D time series are extracted for each participant. Step 2: The $x$ and $y$channels’ signals are transformed by the Hilbert transformation:

$\tilde{x}\left(t\right)={\displaystyle \frac{1}{\pi t}}\ast x\left(t\right)={\displaystyle \frac{1}{\pi }}\underset{+\infty }{\overset{-\infty }{\int }}{\displaystyle \frac{x\left(\tau \right)}{t-\tau }}d\tau$

$\tilde{y}\left(t\right)={\displaystyle \frac{1}{\pi t}}\ast y\left(t\right)={\displaystyle \frac{1}{\pi }}\underset{+\infty }{\overset{-\infty }{\int }}{\displaystyle \frac{y\left(\tau \right)}{t-\tau }}d\tau$

Step 3: The phase of the $x$ and $y$ channels’ signals are calculated from the original signals ($x\left(\tau \right),y\left(\tau \right)$) and transformed into conjugate signals ($\tilde{x}\left(t\right),\tilde{y}\left(t\right)$):

${\phi }_x\left(t\right)=arctan{\displaystyle \frac{\tilde{x}\left(t\right)}{x\left(t\right)}}$

${\phi }_y\left(t\right)=arctan{\displaystyle \frac{\tilde{y}\left(t\right)}{y\left(t\right)}}$

Step 4: According to the phases of the two signals, the average phase difference between the $x$ and $y$ signals is

${PLV}_{xy}={\displaystyle \frac{1}{Len}}\left|{\displaystyle \sum _{n=1}^N}exp\left(j\right(∆{\phi }_n\left(t\right)\left)\right)\right|$

where $∆{\phi }_n={\phi }_x\left(t\right)-{\phi }_y\left(t\right)$ is the phase difference between x and y channels at time t, and $Len$ represents the length of the time series, $PLV\in \left[\mathrm{0,1}\right]$.