def test_eda_clean():
sampling_rate = 1000
eda = nk.eda_simulate(duration=30, sampling_rate=sampling_rate,
scr_number=6, noise=0.01, drift=0.01, random_state=42)
clean = nk.eda_clean(eda, sampling_rate=sampling_rate)
assert len(clean) == len(eda)
# Comparison to biosppy (https://github.com/PIA-Group/BioSPPy/blob/master/biosppy/signals/eda.py)
eda_biosppy = nk.eda_clean(eda, sampling_rate=sampling_rate, method="biosppy")
original, _, _ = biosppy.tools.filter_signal(signal=eda,
ftype='butter',
band='lowpass',
order=4,
frequency=5,
sampling_rate=sampling_rate)
original, _ = biosppy.tools.smoother(signal=original,
kernel='boxzen',
size=int(0.75 * sampling_rate),
mirror=True)
# pd.DataFrame({"our":eda_biosppy, "biosppy":original}).plot()
assert np.allclose((eda_biosppy - original).mean(), 0, atol=1e-5)
def extract_eda_features(eda: List[float], sampling_rate) -> pd.DataFrame:
# eda = MinMaxScaler().fit_transform(np.array(eda).reshape(-1, 1)).ravel()
HIGHCUT_FREQUENCY = 5 # defaults as BioSPPy
nyquist_freq = 2 * HIGHCUT_FREQUENCY / sampling_rate # Normalize frequency to Nyquist Frequency (Fs/2)
if 0 < nyquist_freq < 1:
eda = nk.eda_clean(eda, sampling_rate=sampling_rate, method='biosppy')
eda_decomposed = nk.eda_phasic(eda, sampling_rate=sampling_rate)
scr_peaks, info = nk.eda_peaks(eda_decomposed['EDA_Phasic'], sampling_rate=sampling_rate)
signals = pd.DataFrame({"EDA_Raw": eda})
signals = pd.concat([signals, eda_decomposed, scr_peaks], axis=1)
return signals
def eda_process(raw_signal, exper, path=None):
"""
皮电信号处理，输入未处理的皮电信号，输出scr监测后的图像。
:param raw_signal: 未处理的皮电信号
:param exper: 第几次实验
:param path: 文件保存的地址
:return: 无
"""
# 降采样
downsize_rate = 100
down_size = 2000 / downsize_rate
# raw_signal = np.loadtxt(filepath)
raw_signal = raw_signal[::int(down_size)]
# sampling_rate = int(2000 / down_size)
# plt.plot(raw_signal)
# plt.show()
# 带通滤波与平滑处理
eda_cleaned = nk.eda_clean(raw_signal, sampling_rate=downsize_rate, method="biosppy")
# 相位成分提取
# eda = nk.eda_phasic(eda_cleaned, sampling_rate=sampling_rate, method='median')
# eda_phasic = eda["EDA_Phasic"].values
# plt.plot(eda_phasic)
# plt.show()
# 微分与卷积处理
info = nk.eda_findpeaks(eda_cleaned, sampling_rate=downsize_rate, method="qz")
features = [info["SCR_Onsets"], info["SCR_Offsets"], info['SCR_Peaks'], info['eda_phasic_diffandsmoothed']]
# 时间轴
length = len(eda_cleaned)
T = (length - 1) / downsize_rate
ts = np.linspace(0, T, length, endpoint=True)
# 绘图
# bplt.plot_eda_qz(ts=ts, raw=raw_signal, filtered=eda_cleaned, onsets=features[0],
# offsets=features[1], peaks=features[2], eda_phasic_diffandsmoothed=features[3], show=True,
# path=filepath, stimulus=False)
# 保存文件名
# scr监测与绘图输出保存
if path is not None:
plot_scr_v2(ts=ts, sampling_rate=downsize_rate, filtered=eda_cleaned, onsets=features[0], offsets=features[1],
exper=exper, show=False, path=path)
else:
plot_scr_v2(ts=ts, sampling_rate=downsize_rate, filtered=eda_cleaned, onsets=features[0], offsets=features[1],
exper=exper, show=False)
def standardized_eda_process(eda_signal, sampling_rate, method="neurokit"):
eda_cleaned = nk.eda_clean(eda_signal,
sampling_rate=sampling_rate,
method=method)
eda_cleaned_standardized = nk.standardize(
eda_cleaned) # only change to eda_process
eda_decomposed = nk.eda_phasic(eda_cleaned_standardized,
sampling_rate=sampling_rate)
peak_signal, info = nk.eda_peaks(eda_decomposed["EDA_Phasic"].values,
sampling_rate=sampling_rate,
method=method,
amplitude_min=0.1)
signals = pd.DataFrame({
"EDA_Raw": eda_signal,
"EDA_Clean": eda_cleaned,
"EDA_Standardized": eda_cleaned_standardized
})
signals = pd.concat([signals, eda_decomposed, peak_signal], axis=1)
return signals, info
