Home > biosig > t250 > get_regress_eog.m

get_regress_eog

PURPOSE ^

GET_REGRESS_EOG tries to obtain the regression coefficients

SYNOPSIS ^

function [h0, s00] = get_regress_eog(fn,Mode)

DESCRIPTION ^

 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://www.dpmi.tugraz.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)

CROSS-REFERENCE INFORMATION ^

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