计算脑电信号柯尔莫哥洛夫熵的python代码

以下是一个python代码示例，用于计算脑电信号的柯尔莫哥洛夫熵：

import numpy as np
from scipy.signal import welch

def spectral_entropy(signal, sf, method='fft', nperseg=None, normalize=False):
    """Spectral Entropy.

    Parameters:
    -----------
    signal: list or np.array
        One-dimensional time series of measurement values
    sf: float
        Sampling frequency
    method: str
        Spectral estimation method ::
        'fft' : Fourier Transform (via scipy.signal.welch)
        'welch': Welch periodogram (via scipy.signal.welch)
    nperseg: str or int
        Length of each Welch segment for FFT method.
        If None, uses scipy default of 256 samples.
    normalize: bool
        If True, divides by log2 of frequency bin count
        (some methods use raw spectral entropy, not normalized)

    Returns:
    --------
    se: float
        Spectral Entropy

    Notes:
    ------
    Spectral Entropy is defined to be the Shannon Entropy of the Power
    Spectral Density (PSD) of the data after normalization of the PSD
    to have unit area. This implementation first estimates the PSD
    using welch or FFT, optionally normalizes the PSD, and then applies
    the Shannon Entropy function.

    To reduce memory requirements, the Welch method can be used to divide
    the signal into segments.

    This implementation is based on the spectral_entropy function from
    the Entropy package authored by Raphael Vallat.
    (https://raphaelvallat.com/entropy/build/html/index.html)
    """

    # Compute and normalize power spectrum
    if method == 'fft':
        freqs, psd = welch(signal, sf, nperseg=nperseg)
    elif method == 'welch':
        freqs, psd = welch(signal, sf, nperseg=nperseg)
    else:
        raise ValueError('Method must be "fft" or "welch"')

    psd_norm = np.divide(psd, psd.sum())

    # Compute Shannon entropy
    se = -np.multiply(psd_norm, np.log2(psd_norm)).sum()

    # Normalize according to the spectral resolution
    if normalize:
        se /= np.log2(len(freqs))

    return se

请注意，该代码的详细说明包括输入和输出的每个参数。此外，它使用scipy库中的welch函数来计算功率谱密度。该代码也可以使用其他信号源作为输入，而不仅仅是脑电信号。