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A python package for extracting EEG features. See article "Unsupervised EEG Artifact Detection and Correction" in Frontiers in Digital Health, 2020.

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EEGExtract

Sari Saba-Sadiya1,2, Eric Chantland2, Taosheng Liu2, Tuka Alhanai3, Mohammad Ghassemi1
1 Human Augmentation and Artificial Intelligence lab, Michigan State University, Department of Computer Science
2 Neuroimaging of Perception and Attention Lab, Michigan State University, Department of Psychology
3 Computer Human Intelligence Lab, New York University Abu Dhabi, Department of Electrical and Computer Engineering

A python package for extracting EEG features. First developed for the paper "Unsupervised EEG Artifact Detection and Correction", published in Frontiers in Digital Health, Special issue on Machine Learning in Clinical Decision-Making. Press here for a BibTex citation (or scroll to the bottom of this page).

To the best of our knowledge EEGExtract is the most comprehensive library for EEG feature extraction currently available. This library is actively maintained, please open an issue if you believe adding a specific feature will be of benefit for the community!

Setup

  1. Make sure that you have the required packages listed in requirements.txt. Use pip install -r requirements.txt if unsure.
  2. Simply download and place the EEGExtract.py file in the same folder as your repo. You can then use import EEGExtract as eeg.

License

GPL-3.0 License GPL-3.0 License

Free to use and modify, but must cite the original publication below.

The Features

Signal Descriptor Brief Description Function
Complexity Features degree of randomness or irregularity
Shannon Entropy additive measure of signal stochasticity shannonEntropy
Tsalis Entropy (n=10) non-additive measure of signal stochasticity tsalisEntropy
Information Quantity (δ,α,θ,β,γ) entropy of a wavelet decomposed signal filt_data + shannonEntropy
Cepstrum Coefficients (n=2) rate of change in signal spectral band power mfcc
Lyapunov Exponent separation between signals with similar trajectories lyapunov
Fractal Embedding Dimension how signal properties change with scale hFD
Hjorth Mobility mean signal frequency hjorthParameters
Hjorth Complexity rate of change in mean signal frequency hjorthParameters
False Nearest Neighbor signal continuity and smoothness falseNearestNeighbor
ARMA Coefficients (n=2) autoregressive coefficient of signal at (t-1) and (t-2) arma
Continuity Features clinically grounded signal characteristics
Median Frequency the median spectral power medianFreq
δ band Power spectral power in the 0-3Hz range bandPower
α band Power spectral power in the 4-7Hz range bandPower
θ band Power spectral power in the 8-15Hz range bandPower
β band Power spectral power in the 16-31Hz range bandPower
γ band Power spectral power above 32Hz bandPower
Median Frequency median spectral power medianFreq
Standard Deviation average difference between signal value and it's mean eegStd
α/δ Ratio ratio of the power spectral density in α and δ bands eegRatio
Regularity (burst-suppression) measure of signal stationarity / spectral consistency eegRegularity
Voltage < (5μ, 10μ, 20μ) low signal amplitude eegVoltage
Normal EEG Peak spectral power textgreater= 8Hz
Diffuse Slowing indicator of peak power spectral density less than 8Hz diffuseSlowing
Spikes signal amplitude exceeds μ by 3σ for 70 ms or less spikeNum
Delta Burst after spike Increased δ after spike, relative to δ before spike burstAfterSpike
Sharp spike spikes lasting less than 70 ms shortSpikeNum
Number of Bursts number of amplitude bursts numBursts
Burst length μ and σ statistical properties of bursts burstLengthStats
Burst band powers (δ,α,θ,β,γ) spectral power of bursts burstBandPowers
Number of Suppressions segments with contiguous amplitude suppression numSuppressions
Suppression length μ and σ statistical properties of suppressions suppressionLengthStats
Connectivity Features interactions between EEG electrode pairs
Coherence - δ correlation in in 0-4 Hz power between signals filt_data + coherence
Coherence - All correlation in overall power between signals coherence
Mutual Information measure of dependence calculate2Chan_MI
Granger causality - All measure of causality calcGrangerCausality
Phase Lag Index association between the instantaneous phase of signals phaseLagIndex
Cross-correlation Magnitude maximum correlation between two signals crossCorrMag
Crosscorrelation - Lag time-delay that maximizes correlation between signals corrCorrLagAux

Additionally, EEGExtract also contains implementations for a number of auxiliary functions

Function params Brief Description
filt_data eegData, lowcut, highcut, fs, order=7 midpass filter between lowcut and highcut
fcnRemoveShortEvents z,n z=[chan x samples ], n is threshold
get_intervals A,B,endIdx Find interval of consistent values in binary 1D numpy array

Important Note

The feature extractor is an independent section that can be used with any artifact correction method (recently there have been quite a few including some notable example [1,2]). If you are interested in the specific setup that was used in the paper, as well as a link to the data, please visit the following repository.

[1] S. Phadikar, N. Sinha, and R. Ghosh, “Automatic eeg eyeblink artifact identification and removal technique using independent component analysis in combination with support vector machines and denoising autoencoder”

[2] B. Somers, T. Francart, and A. Bertrand, “A generic eeg artifact removal algorithm based on the multi-channel wiener filter.”

Cite

@article{saba2020unsupervised,
  title={Unsupervised EEG Artifact Detection and Correction},
  author={Saba-Sadiya, Sari and Chantland, Eric and Alhanai, Tuka and Liu, Taosheng and Ghassemi, Mohammad Mahdi},
  journal={Frontiers in Digital Health},
  volume={2},
  pages={57},
  year={2020},
  publisher={Frontiers}
}

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A python package for extracting EEG features. See article "Unsupervised EEG Artifact Detection and Correction" in Frontiers in Digital Health, 2020.

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