Skip to content

Features (Finalised)

Jump to bottom Edit New page
scottclowe edited this page Nov 10, 2014 · 3 revisions

Preprocessing models

  • raw: Raw. No preprocessing. The data as provided from the competition website.
  • cln: Cleaned. Patient_1 and Patient_2 have 60Hz line noise (and its harmonics) removed with a notch reject filter, and are high-pass filtered above 0.1Hz. Very high frequency noise (>300Hz) and artefacts remain. Other sessions are unchanged.
  • dwn: Downsampled. Patient_1 and Patient_2 are downsampled from 5kHz to 400Hz, to match the Dog data (low-pass filtered to Nyquist frequency of 200Hz first).
  • ica: Independent Component Analysis. Data is transformed onto a new basis separating out sources so they are maximally independent. Subject-specific weight matrices are computed from all datapoints in with blind source separation using FastICA algorithm.
  • csp: Common Spatial Patterns. Data is transformed onto a new basis giving maximal separability between the preictal and interictal data sets. Basis transformed channels are ordered from most separable to least seperable. Separability is measured by variance.

The preprocessing models can be stacked, for example 'cln,ica,dwn' will first clean the data (cln), then do an ICA basis transformation (ica), then downsample.

Bands

When bands are used, these are the bands we consider

  • delta: (1-4 Hz)
  • theta: (4-8 Hz)
  • alpha: (8-12 Hz)
  • beta: (12-30 Hz)
  • low_gamma: (30-70 Hz)
  • high_gamma: (70-180 Hz)

Features

  • feat_var: Variance of each channel (single-channel timedomain statistic)
  • feat_lmom-1: 1st order L-moment (mean) (single-channel timedomain statistic)
  • feat_lmom-2: 2nd order L-moment (variance) (single-channel timedomain statistic)
  • feat_lmom-3: 3rd order L-moment (skewness) (single-channel timedomain statistic)
  • feat_lmom-4: 4th order L-moment (kurtosis) (single-channel timedomain statistic)
  • feat_lmom-5: 5th order L-moment (Higher Order Statistic) (single-channel timedomain statistic)
  • feat_lmom-6: 6th order L-moment (Higher Order Statistic) (single-channel timedomain statistic)
  • feat_cov: Covariance of each pair of channels (cross-channel timedomain statistic) [skew-symmetric]
  • feat_corrcoef: Pearson's correlation coefficient (cov normalised by var) (cross-channel timedomain statistic) [skew-symmetric]
  • feat_corrcoefeig: Eigenvalues of corrcoef (cross-channel timedomain summary-statistic)
  • feat_spearman: Spearman's rank correlation (cross-channel timedomain statistic)
  • feat_pib: Power in bands, formed from summing PSD power within each band (single-channel frequency-domain)
  • feat_pib_ratioBB: Ratio of power in each band to the broadband power [1-180Hz] (single-channel frequency-domain)
  • feat_pib_ratio: Ratio of power in bands with each other (single-channel cross-frequency) [skew-symmetric]
  • feat_psd: Power spectral density, linearly sampled in frequency domain (single-channel frequency-domain) [do not use! too many datapoints]
  • feat_coher: Power spectral coherence, linearly sampled in frequency domain (cross-channel frequency-domain) [skew-symmetric] [do not use! too many datapoints]
  • feat_psd_logf: Power spectral density, power within bands logarithmically spaced in frequency domain (single-channel frequency-domain) [use with caution! many datapoints]
  • feat_coher_logf: Power spectral coherence, normalised cross-channel ratio of power within bands logarithmically spaced in frequency domain (cross-channel frequency-domain) [skew-symmetric] [use with caution! many datapoints]
  • feat_act: Auto-correlation width (single-channel timedomain)
  • feat_xcorr-ypeak: Cross-correlation peak normalised against variance, within -5<t<5sec window (cross-channel timedomain) [skew-symmetric]
  • feat_xcorr-tpeak: Cross-correlation peak lag (cross-channel timedomain causal-model) [skew-symmetric]
  • feat_xcorr-twidth: Cross-correlation peak width (cross-channel timedomain) [skew-symmetric]
  • feat_FFT: First 250 coefficients of Fast Fourier Transform of data (single-channel frequencydomain) [do not use! too many datapoints]
  • feat_FFTcorrcoef: Pearson's correlation coefficient between first 250 datapoints of FFT of data (cross-channel frequencydomain statistic) [skew-symmetric]
  • feat_FFTcorrcoefeig: Eigenvalues of FFTcorrcoef (cross-channel frequencydomain summary-statistic)
  • feat_PSDcorrcoef: Pearson's correlation coefficient of PSD of data, sampled every 1Hz (cross-channel frequencydomain statistic) [skew-symmetric]
  • feat_PSDcorrcoefeig: Eigenvalues of PSDcorrcoef (cross-channel frequencydomain summary-statistic)
  • feat_PSDlogfcorrcoef: Pearson's correlation coefficient of PSD of data, sampled logarithmically in frequency domain (cross-channel frequencydomain statistic) [skew-symmetric]
  • feat_PSDlogfcorrcoefeig: Eigenvalues of PSDlogfcorrcoef (cross-channel frequencydomain summary-statistic)
  • feat_phase-#-sync: [#=band] Phase synchrony (cross-channel timedomain) [skew-symmetric]
  • feat_phase-#-dif: [#=band] Phase offset between channels (cross-channel timedomain) [skew-symmetric]
  • feat_ampcorrcoef-#: [#=band] Pearson's correlation coefficient for envelope amplitude of band (cross-channel timedomain statistic) [skew-symmetric]
  • feat_ampcorrcoef-#-eig: [#=band] Eigenvalues of ampcorrcoef (cross-channel timedomain summary statistic) [skew-symmetric]
  • feat_pwling1: Entropy based PairWise Linear-Non-Gaussian model of connection strengths (cross-channel timedomain causal-model) [skew-symmetric]
  • feat_pwling2: First-order approximation, good for sparse variables, of PairWise Linear-Non-Gaussian model of connection strengths (cross-channel timedomain causal-model) [skew-symmetric]
  • feat_pwling4: Skewness measure of PairWise Linear-Non-Gaussian model of connection strengths (cross-channel timedomain causal-model) [skew-symmetric]
  • feat_pwling5: Dodge-Rousson measure, for skewed variables, of PairWise Linear-Non-Gaussian model of connection strengths (cross-channel timedomain causal-model) [skew-symmetric]
  • feat_ilingam-connweights: Independent Component Analysis derived Linear-Non-Gaussian model of connection strengths (cross-channel timedomain causal-model) [skew-symmetric]
  • feat_ilingam-causalorder: Independent Component Analysis derived Linear-Non-Gaussian model of causal ordering of channels [ch4, ch2, ch3, ch1,...] (cross-channel timedomain causal-model)
  • feat_ilingam-causalindex: Independent Component Analysis derived Linear-Non-Gaussian model of causal ordering of each channel [4th, 2nd, 3rd, 1st,...] (cross-channel timedomain causal-model)
  • feat_mvar-ARF: Coefficients for a fitted 12-point MultiVariate-AutoRegressive model (cross-channel timedomain)
  • feat_mvar-COH: Coherence of the coefficients for the MVAR (cross-channel frequencydomain)
  • feat_mvar-COHphs: Phase of Coherence for MVAR (cross-channel frequencydomain)
  • feat_mvar-DC: Directed Coherence of the coefficients for the MVAR (cross-channel frequencydomain causal-model)
  • feat_mvar-DCphs: Phase of Directed Coherence for MVAR (cross-channel frequencydomain)
  • feat_mvar-DTF: Directed Transfer Function of the coefficients for the MVAR (cross-channel frequencydomain causal-model)
  • feat_mvar-DTFphs: Phase of Directed Transfer Function for MVAR (cross-channel frequencydomain)
  • feat_mvar-PCOH: Partial Coherence of the coefficients for the MVAR (cross-channel frequencydomain)
  • feat_mvar-PCOHphs: Phase of Partial Coherence for MVAR (cross-channel frequencydomain)
  • feat_mvar-PDC: Partial Directed Coherence of the coefficients for the MVAR (cross-channel frequencydomain causal-model)
  • feat_mvar-PDCphs: Phase of Partial Directed Coherence for MVAR (cross-channel frequencydomain)
  • feat_mvar-GPDC: Generalized Partial Directed Coherence of the coefficients for the MVAR (cross-channel frequencydomain causal-model)
  • feat_mvar-GPDCphs: Phase of Generalized Partial Directed Coherence for MVAR (cross-channel frequencydomain)
  • feat_mvar-H: Tranfer Function Matrix of the coefficients for the MVAR (cross-channel frequencydomain)
  • feat_mvar-Hphs: Phase of Tranfer Function Matrix for MVAR (cross-channel frequencydomain)
  • feat_mvar-S: Spectral Matrix of the coefficients for the MVAR (cross-channel frequencydomain)
  • feat_mvar-Sphs: Phase of Spectral Matrix for MVAR (cross-channel frequencydomain)
  • feat_mvar-P: Inverse Spectral Matrix of the coefficients for the MVAR (cross-channel frequencydomain)
  • feat_mvar-Pphs: Phase of Inverse Spectral Matrix for MVAR (cross-channel frequencydomain)

[skew-symmetric] = The feature is skew-symmetric, so only the upper-triangle of the combination of pairs is used to prevent redundancy in the data. (The lower-triangle is always the negative of the upper-triangle.) Also, the diagonal is removed since it is always 1 in these cases.

Assumed feature ordering for each feature class

  • Single-channel timedomain statistics: (csp > ica > raw), lmom-3 > lmom-2 > var > lmom-1 > lmom-4 > act > lmom-5 > lmom-6
  • Cross-channel timedomain statistics: (ica > csp > raw), xcorr-ypeak > ampcorrcoef-# > spearman > corrcoef > xcorr-twidth > cov
  • Single-channel frequencydomain: (csp > ica > raw >), pib_ratioBB > pib > psd_logf > FFT
  • Cross-channel frequencydomain: (csp > ica > raw >), mvar-PCOH > mvar-COH > pib_ratio > coher_logf
  • Cross-channel frequencydomain statistics: (ica > csp > raw), PSDlogfcorrcoef > PSDlogfcorrcoefeig > PSDcorrcoef > PSDcorrcoefeig > FFTcorrcoef > FFTcorrcoefeig
  • Causal-model timedomain: (ica > raw > csp), pwling1 > ilingam-connweights > ilingam-causalindex > pwling4 = pwling5 > phase-#-sync > phase-#-diff > mvar-ARF > xcorr-tpeak > pwling2 > ilingam-causalorder
  • Causal-model frequencydomain: (ica > raw > csp), mvar-GPDC > mvar-PDC > mvar-PDC
  • Wildcards: mvar-#phs
  • Of little use: FFT, coher_logf, psd_logf, mvar-H, mvar-S, mvar-P, ilingam-causalorder

You should probably not use more than one feature from each class because they will be correlated with each other.

Also, only use one MVAR feature at a time, as they all all different representations of the same time series model.

Add a custom footer
Add a custom sidebar
Clone this wiki locally