使用Numpy计算EEG频段的FFT错误值

import numpy as np
from scipy.signal import butter, lfilter

# 假设eeg_data是你的EEG信号数据，采样率为sampling_rate
eeg_data = ...  # 你的EEG信号数据
sampling_rate = ...  # 采样率

# 设计带通滤波器以去除噪声
def butter_bandpass(lowcut, highcut, fs, order=5):
    nyq = 0.5 * fs
    low = lowcut / nyq
    high = highcut / nyq
    b, a = butter(order, [low, high], btype='band')
    return b, a

def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):
    b, a = butter_bandpass(lowcut, highcut, fs, order=order)
    y = lfilter(b, a, data)
    return y

# 应用滤波器
filtered_eeg = butter_bandpass_filter(eeg_data, lowcut=0.5, highcut=100, fs=sampling_rate)

# 计算FFT
fft_result = np.fft.fft(filtered_eeg)
freqs = np.fft.fftfreq(len(filtered_eeg), d=1/sampling_rate)

# 提取特定频段的能量
def get_band_energy(fft_result, freqs, lowcut, highcut):
    idx = np.where((freqs >= lowcut) & (freqs <= highcut))
    return np.sum(np.abs(fft_result[idx])**2)

delta_energy = get_band_energy(fft_result, freqs, 0.5, 4)
theta_energy = get_band_energy(fft_result, freqs, 4, 8)
alpha_energy = get_band_energy(fft_result, freqs, 8, 13)

print(f"Delta Energy: {delta_energy}")
print(f"Theta Energy: {theta_energy}")
print(f"Alpha Energy: {alpha_energy}")