spkit.eeg.rhythmic_powers_win

spkit.eeg.rhythmic_powers_win(X, winsize=128, overlap=None, fs=128.0, fBands=[[4], [8, 14]], Sum=True, Mean=False, SD=False, verbose=0, **kwargs)

Rhythmic powers-window-wise

Decompose EEG Signal(s)-all the channels in Rhythms and compute power in each band for each channel

Parameters:
X: array,

  • EEG segment of shape (n,ch) or (n,)

  • where ch is number of channels

winsize: int, default=128

  • size of window for computing powers

overlap: int, default=None

  • overlap shift,

  • number of samples to be shift

  • if None, overlap=winsize//2

fs: int,

  • sampling rate

fBands: list

  • list of frequency bands

  • if None: fBands =[[4],[4,8],[8,14],[14,30],[30,47],[47]]

  • default [[4],[8,14]], ‘delta’ and ‘alpha’

(Sum,Mean,SD): bool, default (True, False, False)

  • if Sum=True, then Total power spectrum in the band computed, default=True

  • if Mean=True, then Average power of the band is computed, default=False

  • if SD=True, then Standard Deviation, (variation) in power for band is computed, default=False

verbose: int, default=0

  • verbosity mode

kwargs:
filter_kwargs: dict, default=dict()

  • arguments for filtering, check filter_X for details

  • default arguments setting is: dict(order=5,ftype=’SOS’,verbose=False)

  • To override any of the argument or suply additional argument based of filter_X,

    provide the in filter_kwargs.

  • For example, filter_kwargs = dict(ftype=’filtfilt’), will overide the ftype.

periodogram_kwargs:dict, default=dict()

  • arguments for periodogram, check periodogram for details

  • default arguments setting is:
    dict(method=’welch’,win=’hann’,scaling=’density’,nfft=None, average=’mean’,

    detrend=’constant’,nperseg=None,noverlap=None,show_plot=False)

  • To override any of the argument or suply additional argument based of periodogram,

    provide the in periodogram_kwargs.

  • For example, periodogram_kwargs = dict(win=’ham’), will overide the win.

filter_warn: bool, default=True

  • It will show warning, if any additional argument other than deafult setting for filter, is provided

  • To turn warning statement off, set filter_warn=False

periodogram_warn=True,

  • It will show warning, if any additional argument other than deafult setting for periodogram is provided

  • To turn warning statement off, set filter_warn=False

Returns:
Pxt: 3d-array.

  • sum of the power in a band - shape (nt, number of bands,nch)

Pmt: 3d-array.

  • mean power in a band - shape (nt, number of bands,nch)

Pdt: 3d-array.

  • standard deviation of power in a band - shape (nt, number of bands,nch)

See also

rhythmic_powers

Examples

#sp.eeg.rhythmic_powers_win
import numpy as np
import matplotlib.pyplot as plt
import spkit as sp
X,fs, ch_names = sp.data.eeg_sample_14ch()
X = sp.filterDC_sGolay(X, window_length=fs//3+1)
t = np.arange(X.shape[0])/fs
Pxt, _, _ = sp.eeg.rhythmic_powers_win(X, winsize=128,overlap=32,fBands=[[4],[8,14],[32,47]],Sum=True)
Pxt = np.log10(Pxt)
plt.figure(figsize=(8,5))
plt.subplot(211)
plt.plot(Pxt[:,:,0], label=[r'$\delta$ (<4 Hz)',r'$\alpha$ (8-14 Hz)',r'$\gamma$ (32 Hz <)',])
plt.xlabel('window #')
plt.ylabel('power (dB)')
plt.title(f'Power of Channel: {ch_names[0]}')
plt.xlim([0,Pxt.shape[0]-1])
plt.grid()
plt.legend(ncol=2,frameon=False)
plt.subplot(212)
plt.plot(t,X[:,0])
plt.grid()
plt.xlim([t[0],t[-1]])
plt.xlabel('time (s)')
plt.ylabel(ch_names[0])
plt.tight_layout()
plt.show()
../../_images/spkit-eeg-rhythmic_powers_win-1.png

Examples using spkit.eeg.rhythmic_powers_win

EEG Topographic Map

EEG Topographic Map