# -*- coding: utf-8 -*-
from warnings import warn
import numpy as np
import pandas as pd
import scipy.signal
from ..misc import NeuroKitWarning, as_vector
from ..signal import signal_filter, signal_smooth
[docs]
def eda_clean(eda_signal, sampling_rate=1000, method="neurokit"):
"""**Preprocess Electrodermal Activity (EDA) signal**
This function cleans the EDA signal by removing noise and smoothing the signal with different methods.
* **NeuroKit**: Default methods. Low-pass filter with a 3 Hz cutoff frequency and a 4th order
Butterworth filter. Note thaht if the sampling rate is lower than 7 Hz (as it is the case
with some signals recorded by wearables such as Empatica), the filtering is skipped (as there
is no high enough frequency to remove).
* **BioSPPy**: More aggresive filtering than NeuroKit's default method. Low-pass filter with a
5 Hz cutoff frequency and a 4th order Butterworth filter.
Parameters
----------
eda_signal : Union[list, np.array, pd.Series]
The raw EDA signal.
sampling_rate : int
The sampling frequency of `rsp_signal` (in Hz, i.e., samples/second).
method : str
The processing pipeline to apply. Can be one of ``"neurokit"`` (default), ``"biosppy"``, or
``"none"``.
Returns
-------
array
Vector containing the cleaned EDA signal.
See Also
--------
eda_simulate, eda_findpeaks, eda_process, eda_plot
Examples
--------
.. ipython:: python
import pandas as pd
import neurokit2 as nk
# Simulate raw signal
eda = nk.eda_simulate(duration=15, sampling_rate=100, scr_number=10, noise=0.01, drift=0.02)
# Clean
eda_clean1 = nk.eda_clean(eda, sampling_rate=100, method='neurokit')
eda_clean2 = nk.eda_clean(eda, sampling_rate=100, method='biosppy')
@savefig p_eda_clean.png scale=100%
nk.signal_plot([eda, eda_clean1, eda_clean2], labels=["Raw", "NeuroKit", "BioSPPy"])
@suppress
plt.close()
"""
eda_signal = as_vector(eda_signal)
# Missing data
n_missing = np.sum(np.isnan(eda_signal))
if n_missing > 0:
warn(
"There are " + str(n_missing) + " missing data points in your signal."
" Filling missing values by using the forward filling method.",
category=NeuroKitWarning,
)
eda_signal = _eda_clean_missing(eda_signal)
method = method.lower() # remove capitalised letters
if method == "biosppy":
clean = _eda_clean_biosppy(eda_signal, sampling_rate)
elif method in ["default", "neurokit", "nk"]:
clean = _eda_clean_neurokit(eda_signal, sampling_rate)
elif method is None or method == "none":
clean = eda_signal
else:
raise ValueError("NeuroKit error: eda_clean(): 'method' should be one of 'biosppy'.")
return clean
# =============================================================================
# Handle missing data
# =============================================================================
def _eda_clean_missing(eda_signal):
eda_signal = pd.DataFrame.pad(pd.Series(eda_signal))
return eda_signal
# =============================================================================
# NK
# =============================================================================
def _eda_clean_neurokit(eda_signal, sampling_rate=1000):
if sampling_rate <= 6:
warn(
"EDA signal is sampled at very low frequency. Skipping filtering.",
category=NeuroKitWarning,
)
return eda_signal
# Filtering
filtered = signal_filter(
eda_signal, sampling_rate=sampling_rate, highcut=3, method="butterworth", order=4
)
return filtered
# =============================================================================
# BioSPPy
# =============================================================================
def _eda_clean_biosppy(eda_signal, sampling_rate=1000):
"""Uses the same defaults as `BioSPPy.
<https://github.com/PIA-Group/BioSPPy/blob/master/biosppy/signals/eda.py>`_.
"""
# Parameters
order = 4
frequency = 5
frequency = (
2 * np.array(frequency) / sampling_rate
) # Normalize frequency to Nyquist Frequency (Fs/2).
# Filtering
b, a = scipy.signal.butter(N=order, Wn=frequency, btype="lowpass", analog=False, output="ba")
filtered = scipy.signal.filtfilt(b, a, eda_signal)
# Smoothing
clean = signal_smooth(
filtered, method="convolution", kernel="boxzen", size=int(0.75 * sampling_rate)
)
return clean
