spkit.data.eeg_sample_artifact

spkit.data.eeg_sample_artifact(fname='EEG16sec_artifact.pkl')

Load 14 channel EEG sample recording of 16 second duration with artifacts and processed.

Recorded sample is a part of PhyAAt Dataset[1]

Returns:

datadictionary contants signals with following keys

‘X_raw’Raw EEG signal

‘X_fil’ : Raw EEG Filtered with high pass filter (0.5Hz) ‘X_ica’ : Aftifact removed using ICA ‘X_atar_soft’ : artifact removed using ATAR soft thresholding mode ‘X_atar_elim’ : artifact removed using ATAR elimination mode

‘fs’ : sampling frequency ‘info’ : information ‘ch_names’ : channel names

See also

eda_sample

Electrodermal activity (EDA)

gsr_sample

Galvanic Skin Response (GSR)

eeg_sample_14ch

Electroencephalography (EEG) - 14-channel

eeg_sample_1ch

Electroencephalography (EEG) - 1-channel

ecg_sample_12leads

Electrocardiogram (ECG) - 12-leads

ecg_sample

Electrocardiogram (ECG) - 1-lead

optical_sample

Optical Mapped Signal

ppg_sample

Photoplethysmogram (PPG)

egm_sample

Electrogram (EGM)

References

• [1] PhyAAt Dataset - https://phyaat.github.io/

Examples

import numpy as np
import matplotlib.pyplot as plt
import spkit as sp
data = sp.data.eeg_sample_artifact()
X = data['X_raw']
fs = data['fs']
ch_names = data['ch_names']
t = np.arange(X.shape[0])/fs
sep = 50 
plt.figure(figsize=(10,6))
plt.plot(t,X + np.arange(X.shape[1])*sep)
plt.xlim([t[0],t[-1]])
plt.xlabel('time (s)')
plt.yticks(np.arange(X.shape[1])*sep,ch_names)
plt.grid()
plt.show()