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Contents


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 Demos of the BIOSIG-toolbox;

 DEMO1	  % QRS-Detection
 DEMO2   % Estimate and validate BCI classifier
 DEMO3	  % Demonstrates how to generate an EDF/GDF/BDF file
 DEMO4	  % Demonstrates how to generate an BKR file
 DEMO5	  % Demonstrates how to generate an WAV file
 DEMO6   % lumped circuit model 
 DEMO7   % Multivariate autoregressive parameters              
 DEMO8   % overflow detection based on [1]
 DEMO9   % AAR-based HRV analysis
 DEMO10  % Demonstrates deconvolution method on spontaneous synaptic currents [2]
 DEMO11  % Demonstrate corrections of EOG artifcts [4,5]
 SLOPE_ESTIMATION: quantifies the error on slope estimation caused by 
         different noise sources (see [3] for details).  
 SIMULATE_EPSP  generates a large number sweeps of EPSP data using
         different models, and parameters for validation of Stimfit model
         fitting algorithms [3] 
 SELECT_SWEEPS: selects and combine sweeps from one or more files



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 Demos of the BIOSIG-toolbox;



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batch


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 BATCH for data processing
 this is a TEMPLATE 



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 BATCH for data processing
 this is a TEMPLATE 




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bench_biosig


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 Benchmark for the BioSig toolbox
  The benchmark test performs a typical data analysis task.
  Except for data loading, it tests the performance of the computational speed
  and can be used to compare the performance of different platforms

  Requirements:
  Octave 2.1 or higher, or Matlab
  BioSig4OctMat from http:/biosig.sf.net/

  References: 
  [1] Alois Schloegl, BioSig - An application of Octave, 2006
      available online: http://arxiv.org/pdf/cs/0603001v1



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 Benchmark for the BioSig toolbox
  The benchmark test performs a typical dat...



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demo10


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 Implement deconvolution method as used in [1]

 References: 
  [1] Alejandro Javier Pernía-Andrade, Sarit Pati Goswami, Yvonne Stickler, 
      Ulrich Fröbe, Alois Schlögl, and Peter Jonas (submitted) 
  A deconvolution-based method with high sensitivity and temporal
  resolution for detection of spontaneous synaptic currents in vitro and
  in vivo. Biophysical Journal Volume 103 October 2012 1–11.



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 Implement deconvolution method as used in [1]



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demo11


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 Demo for EOG regression analysis 

 Copyright (C) 2018 Alois Schloegl <alois.schloegl@gmail.com>
 
 Prerequisites: 
 - Matlab or Octave
 - toolboxes: signal, biosig, nan



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 Demo for EOG regression analysis 



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demo2


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function []=demo2();
 DEMO2 demonstrates the use of the data set III from the BCI competition 2003 for 
   The demo shows the offline analysis for obtaining a classifier and 
   uses a jack-knife method (leave-one-trial out) for validation. 
   AAR parameters are extracted 


 References: 
 [1] A. Schlögl, B. Kemp, T. Penzel, D. Kunz, S.-L. Himanen, A. Värri, G. Dorffner, G. Pfurtscheller.
       Quality Control of polysomnographic Sleep Data by Histogram and Entropy Analysis.
       Clin. Neurophysiol. 1999, Dec; 110(12): 2165 - 2170.
 [2] Alois Schlögl (2000)
       The electroencephalogram and the adaptive autoregressive model: theory and applications
       Shaker Verlag, Aachen, Germany, (ISBN3-8265-7640-3). 
 [3] Schlögl A., Neuper C. Pfurtscheller G.
       Estimating the mutual information of an EEG-based Brain-Computer-Interface
       Biomedizinische Technik 47(1-2): 3-8, 2002.
 [4] A. Schlögl, C. Keinrath, R. Scherer, G. Pfurtscheller,
       Information transfer of an EEG-based Bran-computer interface.
       Proceedings of the 1st International IEEE EMBS Conference on Neural Engineering, Capri, Italy, Mar 20-22, 2003. 
 [5] A. Schlögl, J. Kronegg, J.E. Huggins, S. G. Mason.
       Evaluation criteria in BCI research.
       (Eds.) G. Dornhege, J.R. Millan, T. Hinterberger, D.J. McFarland, K.-R.Müller,
       Towards Brain-Computer Interfacing. MIT Press, p.327-342, 2007.
 [6] A. Schlögl, F.Y. Lee, H. Bischof, G. Pfurtscheller
   	Characterization of Four-Class Motor Imagery EEG Data for the BCI-Competition 2005.
   	Journal of neural engineering 2 (2005) 4, S. L14-L22
 [7] A. Schlögl, C. Brunner, R. Scherer, A. Glatz;
   	BioSig - an open source software library for BCI research.
   	(Eds.) G. Dornhege, J.R. Millan, T. Hinterberger, D.J. McFarland, K.-R. Müller;
   	Towards Brain-Computer Interfacing, MIT Press, 2007, p.347-358. 
 [8] A. Schlögl, C. Brunner
   	BioSig: A Free and Open Source Software Library for BCI Research.
	Computer (2008, In Press)	



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function []=demo2();
 DEMO2 demonstrates the use of the data set III from the...



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demo3


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 Demostration for generating EDF/BDF/GDF-files
 DEMO3 is part of the biosig-toolbox
    and it tests also Matlab/Octave for its correctness. 
 



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 Demostration for generating EDF/BDF/GDF-files
 DEMO3 is part of the biosig-t...



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demo4


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 Demo for Writing BKR files %
 DEMO4 is part of the biosig-toolbox
     it demonstrates generating BKR files 
     and contains a few tests 
 



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 Demo for Writing BKR files %
 DEMO4 is part of the biosig-toolbox
     it de...



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demo5


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 Demo for Writing WAV files %
 DEMO5 is part of the biosig-toolbox
     it demonstrates generating WAV files 
     and contains a few tests 
 



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 Demo for Writing WAV files %
 DEMO5 is part of the biosig-toolbox
     it de...



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demo6


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Demo6 - demonstrates the transfer function of the
        lumped circuit model for various feedback gains

       see also LUMPED

 Reference)(s)
 [1]	Lopes da Silva FH, Hoeks A, Smits H, Zetterberg LH.
	Model of brain rhythmic activity. The alpha-rhythm of the thalamus.
       Kybernetik. 1974 May 31;15(1):27-37.
 [2]   P. Suffcynski, Thesis, 1999.
 [3]   Alois Schlögl (2000)
       The electroencephalogram and the adaptive autoregressive model: theory and applications
       Shaker Verlag, Aachen, Germany,(ISBN3-8265-7640-3). 



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Demo6 - demonstrates the transfer function of the
        lumped circuit mode...



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demo7


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 Demo7 - MULTIVARIATE AUTOREGRESSIVE Analysis: 

      1) Simulates a MVAR process
      2) Estimatess MVAR parameters
      3) Displays the PDC for the original parameters and its estimates

   demo7(k)
       k       if k is a skalar between 1 to 5: use simulation k from Baccala et al. [1] 
   demo7(eeg)
       demonstrates the simulation of Kus et al. [3]        

 see also: BACCALA2001, MVAR, MVFILTER, PLOTA, demo/demo7

 Reference(s):
  [1] Baccala LA, Sameshima K. (2001)
       Partial directed coherence: a new concept in neural structure determination.
       Biol Cybern. 2001 Jun;84(6):463-74. 
  [2] M. Kaminski, M. Ding, W. Truccolo, S.L. Bressler, Evaluating causal realations in neural systems:
	Granger causality, directed transfer functions and statistical assessment of significance.
	Biol. Cybern., 85,145-157 (2001)
  [3] R. Kus, M. Kaminski, K.J.Blinowska, Determination of EEG Activity Propagation - 
       Pairwise vs. Multichannel Estimate. IEEE Trans. Biomedical
       Engineering 51(9) 1501-1510 (Sep 2004);
  [4] http://biosig.sf.net/



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 Demo7 - MULTIVARIATE AUTOREGRESSIVE Analysis: 



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demo9


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 DEMO2 demonstrates the use of the data set III from the BCI competition 2003 for 
   The demo shows the offline analysis for obtaining a classifier and 
   uses a jack-knife method (leave-one-trial out) for validation. 
   AAR parameters are extracted 


 References: 
 [1] A. Schlögl, B. Kemp, T. Penzel, D. Kunz, S.-L. Himanen, A. Värri, G. Dorffner, G. Pfurtscheller.
       Quality Control of polysomnographic Sleep Data by Histogram and Entropy Analysis.
       Clin. Neurophysiol. 1999, Dec; 110(12): 2165 - 2170.
 [2] Alois Schlögl (2000)
       The electroencephalogram and the adaptive autoregressive model: theory and applications
       Shaker Verlag, Aachen, Germany, (ISBN3-8265-7640-3). 
 [3] Schlögl A., Neuper C. Pfurtscheller G.
       Estimating the mutual information of an EEG-based Brain-Computer-Interface
       Biomedizinische Technik 47(1-2): 3-8, 2002.
 [4] A. Schlögl, C. Keinrath, R. Scherer, G. Pfurtscheller,
       Information transfer of an EEG-based Bran-computer interface.
       Proceedings of the 1st International IEEE EMBS Conference on Neural Engineering, Capri, Italy, Mar 20-22, 2003. 
 [5] A. Schlögl, J. Kronegg, J.E. Huggins, S. G. Mason.
       Evaluation criteria in BCI research.
       (Eds.) G. Dornhege, J.R. Millan, T. Hinterberger, D.J. McFarland, K.-R.Müller,
       Towards Brain-Computer Interfacing. MIT Press, p.327-342, 2007.
 [6] A. Schlögl, F.Y. Lee, H. Bischof, G. Pfurtscheller
   	Characterization of Four-Class Motor Imagery EEG Data for the BCI-Competition 2005.
   	Journal of neural engineering 2 (2005) 4, S. L14-L22
 [7] A. Schlögl, C. Brunner, R. Scherer, A. Glatz;
   	BioSig - an open source software library for BCI research.
   	(Eds.) G. Dornhege, J.R. Millan, T. Hinterberger, D.J. McFarland, K.-R. Müller;
   	Towards Brain-Computer Interfacing, MIT Press, 2007, p.347-358. 
 [8] A. Schlögl, C. Brunner
   	BioSig: A Free and Open Source Software Library for BCI Research.
	Computer (2008, In Press)	



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 DEMO2 demonstrates the use of the data set III from the BCI competition 2003...



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make_cc7


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 Builds a classifier based on AAR parameters from BCI recordings. 
  The result includes also initial values for the next run. 

  MAKE_CC is obsolete and will be removed in future versions. Use DEMO2 instead. 

 CC=make_cc(fn,eegchan,trigchan,Fs)
  
  e.g. 
  CC=make_cc('x21fb*')
  CC=make_cc({'x21fb1.mat','x21fb2.mat'})
  CC=make_cc(fn,[1,3],4,128)

 default: 
 	eegchan=[1,3];
 	trigchan=4;
 	Fs=128;

 References:
 [1] Schlögl A., Neuper C. Pfurtscheller G. (2002)
   Estimating the mutual information of an EEG-based Brain-Computer-Interface.
   Biomedizinische Technik 47(1-2): 3-8, 2002
 [2] A. Schlögl, C. Keinrath, R. Scherer, G. Pfurtscheller, (2003)
   Information transfer of an EEG-based Bran-computer interface.
   Proceedings of the 1st International IEEE EMBS Conference on Neural Engineering, Capri, Italy, Mar 20-22, 2003. 
 [3] A. Schlögl, D. Flotzinger and G. Pfurtscheller (1997)
   Adaptive Autoregressive Modeling used for Single-trial EEG Classification
   Biomedizinische Technik 42: (1997), 162-167. 
 [4] A. Schlögl, C. Neuper and G. Pfurtscheller (1997)
   Subject-specific EEG pattern during motor imagery
   Proceedings of the 19th Annual International Conference if the IEEE Engineering in Medicine and Biology Society , vol 19, pp.1530-1532, 1997.
 [5] A. Schlögl, K. Lugger and G. Pfurtscheller (1997)
   Using Adaptive Autoregressive Parameters for a Brain-Computer-InterfaceExperiment,
   Proceedings of the 19th Annual International Conference if the IEEE Engineering in Medicine and Biology Society ,vol 19 , pp.1533-1535, 1997.




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 Builds a classifier based on AAR parameters from BCI recordings.



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scptest


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 SCPTEST checks files for SCP-ECG dataformat. 

  scptest(p)
	checks recursively all files in the subdirectory p
  scptest(f)
	checks file f 

  fid = fopen('logfile.log','w');
  scptest(p,fid)
     redirects output into logfile with open handle fid. 


 see also: SOPEN, SCLOSE, SCPOPEN

 References: 
 [1] The OpenECG project: http://www.openecg.org/ 




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 SCPTEST checks files for SCP-ECG dataformat.



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select_sweeps


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 SELECT_SWEEPS is a program to combine and select sweeps from 1 or more recordings

 Usage:
    [data,HDR] = select_sweeps(outfile, infile1, swlist1)
 	select sweeps
    [data,HDR] = select_sweeps(outfile, {infile1, swlist1, infile2, swlist2,..});
       combine sweeps from multiple files

    outfile: 	output filename
    infileX:	input filename(s)
    swlistX: 	list of sweep numbers (1-indexed)

 Copyright (C) 2024,2025 Alois Schlögl, ISTA

    BioSig is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    BioSig is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with BioSig.  If not, see <http://www.gnu.org/licenses/>.



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 SELECT_SWEEPS is a program to combine and select sweeps from 1 or more recor...



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simulate_epsp


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 SIMULATE_EPSP  generates a large number sweeps of EPSP data using
         different models, and parameters for validation of Stimfit model
         fitting algorithms [3] 
 For each model, a separate GDF file with sweeps of varying model 
    paramters is generated. 



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 SIMULATE_EPSP  generates a large number sweeps of EPSP data using
         d...



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slope_evaluation


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 SLOPE_EVALUATION quantify the error on slope estimation caused by 
         different noise sources, and sampling rates (see [3] for details). 

 evaluate slope analysis, and its influence of windowlength, noise level, sampling rate, 

 change sampling rate and noise level, estimated max slope as function of 



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 SLOPE_EVALUATION quantify the error on slope estimation caused by 
         ...





