注释脑电状态的python代码

以下是注释脑电状态的Python代码示例：

# 导入必要的库
import numpy as np
import scipy.signal as signal
import matplotlib.pyplot as plt

# 读取脑电数据文件
eeg_data = np.loadtxt('eeg_data.txt')

# 定义滤波器参数
nyquist_freq = 0.5 * 256  # 采样率为256Hz
low_cutoff = 1  # 低通滤波器截止频率为1Hz
high_cutoff = 40  # 高通滤波器截止频率为40Hz
filter_order = 4  # 滤波器阶数为4

# 应用带通滤波器
b, a = signal.butter(filter_order, [low_cutoff / nyquist_freq, high_cutoff / nyquist_freq], btype='band')
eeg_filtered = signal.filtfilt(b, a, eeg_data)

# 计算脑电信号的功率谱密度
freqs, psd = signal.welch(eeg_filtered, fs=256, nperseg=256)

# 绘制功率谱密度图
plt.plot(freqs, psd)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Power Spectral Density (V^2 / Hz)')
plt.show()

# 根据功率谱密度估计脑电状态
delta_power = np.trapz(psd[(freqs >= 1) & (freqs <= 4)])
theta_power = np.trapz(psd[(freqs >= 4) & (freqs <= 8)])
alpha_power = np.trapz(psd[(freqs >= 8) & (freqs <= 13)])
beta_power = np.trapz(psd[(freqs >= 13) & (freqs <= 30)])
gamma_power = np.trapz(psd[(freqs >= 30) & (freqs <= 40)])

if delta_power > theta_power and delta_power > alpha_power and delta_power > beta_power and delta_power > gamma_power:
    print('脑电状态为 Delta')
elif theta_power > alpha_power and theta_power > beta_power and theta_power > gamma_power:
    print('脑电状态为 Theta')
elif alpha_power > beta_power and alpha_power > gamma_power:
    print('脑电状态为 Alpha')
elif beta_power > gamma_power:
    print('脑电状态为 Beta')
else:
    print('脑电状态为 Gamma')

这段代码首先读取脑电数据文件，然后通过带通滤波器对数据进行滤波，接着计算脑电信号的功率谱密度，并根据功率谱密度估计脑电状态。最后，根据脑电状态输出相应的结果。