spkit.filter_X

spkit.filter_X(X, fs=128.0, band=[0.5], btype='highpass', order=5, ftype='filtfilt', verbose=False, use_joblib=False, filtr_keywors={})

Spectral filtering using Buttorworth

Buttorworth filtering - basic filtering

Wrapper for spectral filtering.

Parameters:
Xarray, 1d, 2d, (n,) or (n,ch),

  • input signal single channel (n,) or multi-channel, channel axis should be 1 shape ~ (n,ch)

fs: scaler, default=128

  • smapling frequency

band: list of one or two scaler values.

  • cut of frequency, for lowpass and highpass, band is list of one, for bandpass list of two numbers

btype: str, {‘highpass’,’lowpass’,’bandpass’,’bandstop’}

  • band of filter

order: int, positive, default=5,

  • order of filter

ftype: {‘filtfilt’, ‘lfilter’, ‘SOS’, ‘sosfilt’,’sosfiltfilt’}

  • filtering approach type, ‘SOS’ is mapped to ‘sosfiltfilt’

  • lfilter is causal filter, which introduces delay, filtfilt does not introduce any delay, but it is non-causal filtering

SOS: Filter a signal using IIR Butterworth SOS method. A forward-backward digital filter using cascaded second-order sections. NOTE: ‘SOS’ is Recommended

Returns:
Xf: filtered signal of same size as X

See also

filter_smooth_sGolay

Smoothing signal using Savitzky-Golay filter

filter_smooth_gauss

Smoothing signal using Gaussian function

filter_smooth_mollifier

Smoothing signal using mollifier

filter_with_kernel

Filtering signal using provided kernel

filtering_pipeline

Filtering pipeline

filter_powerline

Filtering out the powerline interference

wavelet_filtering

Wavelet Filtering

Notes

  • Filtering using Buttorworth

References

Examples

import numpy as np
import matplotlib.pyplot as plt
import spkit as sp
x,fs = sp.load_data.eeg_sample_1ch()
t = np.arange(len(x))/fs
x1 = sp.filter_X(x.copy(),fs=128.0, band=[4], btype='lowpass',ftype='SOS')
x2 = sp.filter_X(x.copy(),fs=128.0, band=[8,12], btype='bandpass',ftype='SOS')
x3 = sp.filter_X(x.copy(),fs=128.0, band=[32], btype='highpass',ftype='SOS')
X = np.c_[x,x1,x2,x3]
plt.figure(figsize=(12,5))
plt.plot(t,X + np.arange(X.shape[1])*50)
plt.xlim([t[0],t[-1]])
plt.yticks(np.arange(X.shape[1])*50, ['Raw EEG','<4Hz (Lowpass)', '8-12Hz (Bandpass)','32<(Highpass)'])
plt.xlabel('time (s)')
plt.show()
../../_images/spkit-filter_X-1.png

Examples using spkit.filter_X

EEG Data from EDF File

EEG Data from EDF File