GET_REGRESS_EOG tries to obtain the regression coefficients
for EOG correction. According to [1], some extra recordings
with large eye movements (i.e. EOG artifacts) are needed.
GET_REGRESS_EOG tries to identify this data.
hdr = get_regress_eog(file)
hdr = get_regress_eog(file, Mode)
INPUT:
file filename which should be corrected.
usually, the eye movements are stored in a different file.
Some lab-specific heuristics is used to identify the file with the eye movements.
Mode 'REG' [default] regression with one or two bipolar EOG channels [1]
'REG+CAR' regression and common average reference
removes 2 bipolar + averaged monopolar EOG
'REG+PCA' regression and PCA,
removes 3 "EOG" components
'REG+ICA' regression + ICA [3]
removes 3 "EOG" components
'PCA-k' removes the k-largest PCA components, k must be a positive integer
'ICA-k' removes the k-largest ICA components [3], k must be a positive integer
others like 'NGCA-k','TDSEP-k','TDSEP3-k','TDSEP1','FFDIAG'
'msec' same as PCA-3, modified (without averaging) MSEC method [2]
'bf-' beamformer, assume zero-activity reference electrode
'bf+' beamformer, take into account activity of reference electrode
'Hurst' ICA components selected by the method of[5]
'Joyce2004' [6]
'Barbati2004' [7]
'Meinecke2002' [8]
The following modifiers can be combined with any of the above
'FILT###-###Hz' filtering between ### and ### Hz. ### must be numeric
'Fs=###Hz' downsampling to ### Hz, ### must be numeric
'x' 2nd player of season2 data
OUTPUT:
hdr.REGRESS.r0 correction coefficients
The EOG correction will be applied to the channels CHAN with any of these commands:
HDR = sopen(file,'r',hdr.REGRESS.r0(:,CHAN)); [s,HDR]=sread(HDR); HDR=sclose(HDR);
[s,HDR] = sload(file,hdr.REGRESS.r0(:,CHAN));
[s,HDR] = sload(file,CHAN,'EOG_CORRECTION','ON');
See also: SLOAD, IDENTIFY_EOG_CHANNELS, BV2BIOSIG_EVENTS, REGRESS_EOG
Reference(s):
[1] Schlogl A, Keinrath C, Zimmermann D, Scherer R, Leeb R, Pfurtscheller G.
A fully automated correction method of EOG artifacts in EEG recordings.
Clin Neurophysiol. 2007 Jan;118(1):98-104. Epub 2006 Nov 7.
http://dx.doi.org/10.1016/j.clinph.2006.09.003
http://pub.ist.ac.at/~schloegl/publications/schloegl2007eog.pdf
[2] Berg P, Scherg M.
A multiple source approach to the correction of eye artifacts.
Electroencephalogr Clin Neurophysiol. 1994 Mar;90(3):229-41.
[3] JADE algorithm, Jean-François Cardoso.
[4] Boudet S., Peyrodie L., P Gallois, C Vasseur,
Filtering by optimal projectsion and application to automatic artifact removal from EEG
Signal Processing 87 (2007) 1987-1992.
[5] Vorobyov and Cichocki (2002)
Blind noise reduction for multisensory signals using ICA and subspace
filtering, with application to EEG analysis.
Biol Cybern. 2002 Apr;86(4):293-303.
[6] C.A. Joyce, I.F. Gorodnitsky, M.Kutas
Automated removal of eye movement and blink artifats from EEG data using blind component separation.
Psychobiology, 41 (2004), 313-325
[7] Barbati et al (2004)
[8] Frank Meinecke, Andreas Ziehe, Motoaki Kawanabe, and Klaus-Robert Müller.
A Resampling Approach to Estimate the Stability of One-Dimensional or Multidimensional Independent Components.
IEEE Transactions on Biomedical Engineering, 49(12):1514-1525, 2002.
[9] Blanchard G., Kawanabe M., Sugiyama M., Spokoiny V., Muller K.-R. (2006).
In search of non-gaussian components of a high-dimensional distribution.
Journal of Machine Learning Research 7, 247-282.
[10] Kawanabe M., Sugiyama M., Blanchard G, Müller K.-R. (2007)
A new algorithm of non-Gaussian component analysis with radial kernel functions
Annals of the Institute of Statistical Mathematics, 59(1):2007
[11] K.H. Ting, P.C.W. Fung, C.Q.Chang, F.H.Z.Chan
automatec correction of artifact from single-trial event-related potentials bz blind separation using second order statistics only.
Medical Engineering & Physics, 28, 780-794 (2006)