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)

- identify_eog_channels IDENTIFY_EOG_CHANNELS returns bipolar EOG channels for
- regress_eog REGRESS_EOG yields the regression coefficients for
- rs [y2] = rs(y1, T) resamples y1 to the target sampling rate y2 using T

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