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Contents


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 BIOSIG/T300 contains signal processing functions for Matlab/Octave
     It requires also the TSA-toolbox [1].

 Currently the following methods are supported:  
   Multivariate Autoregressive Analysis   
       baccala2001
       mvar
       mvfilter
	mvfreqz
   Time-varying Autoregressive spectral estimation  
       aar, amarma
       tfar
	tvaar	wrapper for AAR estimators
   Time-varying Multivariate Autoregressive Analysis
       mvar
       mvaar
       tfmvar
   EEG 
       lumped model
       kemp's feedback loop model
	evoked potential 
	arspectrum
	cfm - cerebral function monitor
   EMG analysis
       Paynter
   ECG analysis
	qrsdetect
       ecg_wave_analysis (not complete yet)
	berger
       ecgbcorr
       qrscorr
       tvaar
	heartratevariability
   SaO2 - Oxygen saturation
	desatur
   Bloodpressure
	abp - arterial blood pressure pulse detection 
   Others:
	Brainrate (including SEF90, SEF95)
       Hjorth
       Bandpower
	Wackermann
	Hurst (Hurst coefficients)
 

 REFERENCES: 
  [1]  A. Schloegl, Time Series Analysis toolbox for Matlab and Octave. 1996-2004.
     available online: http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/tsa/download.html





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 BIOSIG/T300 contains signal processing functions for Matlab/Octave
     It r...



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abp


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 ABP is Arterial blood pressure (ABP) pulse detector 
   The algorithm is based on [1]  

  pos = abp(x,fs);
 

  x	blood pressure signal 
  fs   sampling rate
  pos  (tentative) position of blood pressure onset. 


 Reference(s):
 [1] Zong, W.; Heldt, T.; Moody, G.B.; Mark, R.G.
     An open-source algorithm to detect onset of arterial blood pressure pulses
     Computers in Cardiology, 2003
     Volume , Issue , 21-24 Sept. 2003 Page(s): 259 - 262



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 ABP is Arterial blood pressure (ABP) pulse detector 
   The algorithm is bas...



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ap_detect


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 AP_DETECT - detection of action potentionsl 

   HDR = ap_detect(fn,chan,Mode)
   HDR = ap_detect(s,Fs,Mode)

 INPUT
   fn	        filename
   chan        channel number of ecg data
   s           ap signal data 
   Fs          sample rate 
   Mode        optional - default is 1
               1: exceed slope of 20 V/s
		2: 
 OUTPUT
   HDR.EVENT  fiducial points of the action potential	


 see also: QRSDETECT, EVENTCODES.TXT, SLOAD, 

 Reference(s):





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 AP_DETECT - detection of action potentionsl 



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arspectrum


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 Spectral analysis using autoregressive method. 

  X = arspectrum(s,Fs,PhysDim)
       s  	signal data
       Fs      Sampling Rate
       PhysDim   physical units of s

  X = arspectrum(filename)
  X = arspectrum(filename,CHAN)
       filename must be a signal format known by BIOSIG
       
  Result can be displayed with PLOTA(Q)          

  see also: SLOAD, PLOTA, TSA/LATTICE, TSA/DURLEV



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 Spectral analysis using autoregressive method.



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baccala2001


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 BACCALA2001 returns the MVAR-Parameters for 
    simulating MVAR processes according to [1].  

  [A1,A2,A3,A4,A5,X6,X7] = baccala2001; 
       A1 ... A5 are 5 different sets of MVAR parameters
  baccala2001(k1:k2); 
       displays for Ak1 ... Ak2 corresponding PDC and DTF      
       
 Simulated MVAR process can be produced with 
       M = size(Ak,1); N = 1000;
       y = mvfilter(eye(M),[eye(M),-Ak],randn(M,N));  

 see also: PLOTA, MVAR, MVFILTER 

 REFERENCES:
  [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]	Yonghong Chen, Steven L. Bressler, Mingzhou Ding (2006)
	Frequency decomposition of conditional Granger causality and
	application to multivariate neural field potential data
	Journal of Neuroscience Methods 150 (2006) 228–237
  [3]  Winterhalter et al 2005, Signal Processing 85 (2005) 2137-2160
 	Comparison of linear signal processing techniques to infer directed 
 	interactions in multivariate neural systems.



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 BACCALA2001 returns the MVAR-Parameters for 
    simulating MVAR processes a...



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bandpower


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 BANDPOWER calculation
       bp = bandpower(s, Fs, bands, smoothing, mode)

 INPUT:
    s          raw data, one channel per column
    Fs         sampling rate
    bands      each row has two elements indicating the lower and upper frequency
               default value is [10,12;16,24] indicating two bands of
               10-12 and 16-24 Hz.
    smoothing  length of smoothing window in seconds. The default value is 1 [s]
 		for mode==6 (Hilbert transform) this parameter is ignored.
    mode       mode == 1 uses FIR filter and log10
               mode == 2 uses Butterworth IIR filter and log10
               mode == 3 udes FIR filter and ln
               mode == 4 uses Butterworth IIR filter and ln
               mode == 5 uses FFT filter and ln
               mode == 6 uses Hilbert transform, and returns log(envelope^2)
               the default value is mode == 4 (Butterworth filter and ln)

 OUTPUT:
    bp is log(bandpower) of s
       the order of the features is
       [f1(#1), f1(#2),...,f1(#n), f2(#1),...,f2(#n),...,fm(#1),...,fm(#n)]
       First, the first frequency band of all channels, is followed by
       the the second band of all channels, until the last
       last f-band of all channels




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 BANDPOWER calculation
       bp = bandpower(s, Fs, bands, smoothing, mode)



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barlow


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undocumented function: [A, F, S] = barlow (S, UC, A)


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undocumented function: [A, F, S] = barlow (S, UC, A)



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berger


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 Resampling with the Berger algorithm
 [HRV,RRI] = berger(RRI, Fs)
 [HRV,RRI] = berger(ONSET, Fs)
 [HRV,RRI] = berger(HDR, Fs)
 
 RRI 	R-to-R interval 
 ONSET onset time QRS-complex
 Fs	target sampling rate
 HDR	header struct as defined by SOPEN, SLOAD. 
 	HDR.EVENT must contain the QRS events
 HRV 	heart rate variability sampled with Fs
 RRI	R-to-R interval sampled with Fs

 Reference(s):
 [1] Berger RD, Akselrod S, Gordon D, Cohen RJ. 
    An efficient algorithm for spectral analysis of heart rate variability.
    IEEE Trans Biomed Eng. 1986 Sep;33(9):900-4.



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 Resampling with the Berger algorithm
 [HRV,RRI] = berger(RRI, Fs)
 [HRV,RRI]...



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brainrate


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undocumented function: [br, sef90, sef95, br2] = brainrate (s, Fs, UC, A)


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undocumented function: [br, sef90, sef95, br2] = brainrate (s, Fs, UC, A)



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bss


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  BSS is a wrapper for various Blind Source Separation algorithms

  W = bss(data, Mode)
  W = bss(data, Mode, M)
  W = bss(data, Mode, [], maxlag)
  W = bss(data, Mode, M, maxlag)

  W: 	   unmixing matrix
  data:   signal data (each column is a channel)
  M: 	   [optional] number of components.
  maxlag: maximum lag for time delayed separation 	
  Mode:   algorithm used. Currently are supported: 
	PCA
	FastICA [6]
	JADE [1-3]
	NGCA
 	FFDIAG [4-5]
	TDSEP (old not recommended)
	TDSEP1 [4-5]
	TDSEP3 [4-5]

 References:
 [1] Cardoso, Jean-François; Souloumiac, Antoine (1993).
   Blind beamforming for non-Gaussian signals.
   IEE Proceedings F (Radar and Signal Processing). 140 (6): 362–370.
 [2] http://perso.telecom-paristech.fr/~cardoso/guidesepsou.html
 [3] http://perso.telecom-paristech.fr/~cardoso/Algo/Jade/jade.m
 [4] A. Ziehe, G.Nolte, K-R. Mueller
   A Fast Algorithm for Joint Diagonalization with Non-orthogonal
   Transformations and its Application to Blind Source Separation.
   Journal of Machine Learning Research 5 (2004) 777–800
   http://www.jmlr.org/papers/volume5/ziehe04a/ziehe04a.pdf
 [5] http://www.user.tu-berlin.de/aziehe/code/
 [6] https://research.ics.aalto.fi/ica/fastica/



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  BSS is a wrapper for various Blind Source Separation algorithms



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burst_onset_phase


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 BURST_ONSET_PHASE computes the phase of burst onsets 

 ... = burst_onset_phase(fn, ch, [f1, f2], EVT)
 ... = burst_onset_phase(..., '-o', outputFilename)
 ... = burst_onset_phase(s, HDR, [f1, f2], EVT)
 [RES, HDR] = burst_onset_phase(...)
 
 Input: 
  fn	filename 
  ch	channel number(s), default=0 (i.e. all)	
  s	signal data 
  HDR  header structure
  [f1,f2]   edge frequencies of bandpass filter
  EVT  header structure containing events of 
       burst onset (0x0202) and 
       start of new segments (0x7ffe)	

 Output:
  RES  a complex matrix containing normalized Amplitude and Phase 
	for each burst onset (number of rows) and for 
	each channel (number of columns). The amplitude is 
 	normalized with the rms of the filtered signal. 
  angle(RES) returns the phase at burst onset



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 BURST_ONSET_PHASE computes the phase of burst onsets 



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cfm


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 CFM - cerebral function monitor 
       c = CFM(filename)
       c = CFM(s,Fs)
       c = CFM(s,HDR)



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 CFM - cerebral function monitor 
       c = CFM(filename)
       c = CFM(s,F...



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correlation_with_reference


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 CORRELATION_WITH_REFERENCE estimates the the correlation of each 
    channel with the (common, global) activity at the references
    electrode.

  R = CORRELATION_WITH_REFERENCE(filename)
  R = CORRELATION_WITH_REFERENCE(data)




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 CORRELATION_WITH_REFERENCE estimates the the correlation of each 
    channe...



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csp


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 CSP computes common spatial patterns
 	this version supports multiple classes using a One-vs-Rest scheme

 [V] = csp(ECM)
 [V] = csp(X,Y)
 [V] = csp(...,Mode)

 ECM(k,:,:) is the extended covariance matrices (see COVM) for class k  
 X,Y	are matrices of the two classes (one channel per column)
	the number of columns must be the same for X and Y
 Mode  = 'CSP0' uses common diagonalization 
	= 'CSP3' solves generalized eigenvalue problem
 V	each column represents one CSP component. 

 REFERENCES: 
 [1] Koles ZJ, Soong AC.
 	EEG source localization: implementing the spatio-temporal decomposition approach.
 	Electroencephalogr Clin Neurophysiol. 1998 Nov;107(5):343-52
 [2] Ramoser, H.; Muller-Gerking, J.; Pfurtscheller, G.;
 	Optimal spatial filtering of single trial EEG during imagined hand movement
	Rehabilitation Engineering, IEEE Transactions on [see also IEEE Trans. on Neural Systems and Rehabilitation]
	Volume 8,  Issue 4,  Dec. 2000 Page(s):441 - 446 
 [3] Dornhege, G.; Blankertz, B.; Curio, G.; Muller, K.-R.;
    	Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms
 	Biomedical Engineering, IEEE Transactions on
 	Volume 51,  Issue 6,  June 2004 Page(s):993 - 1002
	Digital Object Identifier 10.1109/TBME.2004.827088 
 [4] Lemm, S.; Blankertz, B.; Curio, G.; Muller, K.-R.;
    	Spatio-Spectral Filters for Improving the Classification of Single Trial EEG
 	Biomedical Engineering, IEEE Transactions on
 	Volume 52,  Issue 9,  Sept. 2005 Page(s):1541 - 1548
	Digital Object Identifier 10.1109/TBME.2005.851521 



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 CSP computes common spatial patterns
 	this version supports multiple classe...



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desatur


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 DESATUR calculates desaturation of the Oxygen in the blood
 [V,s,ODI,x]=desatur(FN [,CH]) 
 [V,s,ODI,x]=desatur(s,Fs)

 FN    filename 
 CH    channel number, default 16.
 s 	recorded signal,
 Fs    sampling rate

 Automatic evaluation of oxygen saturation can deliver:
 V(1) - mean oxygen saturation
 V(2) - time spent with oxygen saturation below 90% in minutes
 V(3) - time spent with oxygen saturation below 80% in minutes
 V(4) - time spent with oxygen saturation below 70% in minutes
 V(5) - number of oxygen desaturations
 V(6) - mean value of oxygen desaturation in percent
 V(7) - mean duration of oxygen desaturation in seconds
 V(8) - median oxygen saturation
 V(9) - median value of oxygen desaturation in percent
 V(10)- median duration of oxygen desaturation in second
 
 s   original Sa02 signal
 ODI Oxygen de-saturation index (number of detected de-saturations per hour)
 x   detector output

  see also: QRSDETECT

  $Id$
  Copyright (C) 2000,2007 by Alois Schloegl 
  This is part of the BIOSIG-toolbox http://biosig.sf.net/



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 DESATUR calculates desaturation of the Oxygen in the blood
 [V,s,ODI,x]=desa...



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ecg_wave_analysis


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 ECG_WAVE_ANALYSIS - analysis basic ECG waves, like P-, QRS- and T-wave

 Currently, the function is incomplete, only a framework for the event
 handling is implemented, but no actuall detection algorithms.

   HDR = ecg_analysis(fn,chan,Mode)
   ... = ecg_analysis(fn, 0, Mode, '-o',outputFilename)
   ... = ecg_analysis(... ,'-e',eventFilename)
   HDR = qrsdetect(s,Fs,Mode) 

 INPUT
   	fn	filename
   	chan    channel number of ecg data
		if chan is empty, all channels which contain ECG, ecg, or EKG in HDR.Label 
		are used. 
   	s       ecg signal data 
   	Fs      sample rate 
	outputFilename
		name of file for storing the resulting data with the
		detected spikes and bursts in GDF format.
		
	eventFilename
		filename to store event inforamation in GDF format. this is similar to 
		the outputFile, except that the signal data is not included and is, therefore,
		much smaller than the outputFile

 OUTPUT
   HDR.EVENT  event table containing beginning and end of P, QRS and T wave


 see also: SLOAD, QRSDETECT

 Reference(s):
 [1] The CSE Working Party. Recommendations for measurement standards in
     quantitvative electrocardiography. European Heart Journal (1985) 6,
     815-825





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 ECG_WAVE_ANALYSIS - analysis basic ECG waves, like P-, QRS- and T-wave



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ectbcorr


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 Correction of ectopic beat Presence Effect

 This Algorithm corrects the effect of ectopic beats, annotated
    by the function QRScorr, by calculating the corrected Heart Timing Signal,
    according to the IPFM - Modell
 With the Heart Timing Signal the HRV PSD estimation can be calculated by the 
    FHTIS - method. (see [1])
 
 INPUT:
  QRStime:      Time values of the detected QRS-Complexes
  ectb_times:   Time values of the ectopic beats generated by
                   QRScorr(output-variable: ANNOT.mov)

 OUTPUT:
  ht:           ht.data: Heart Timing Signal
                ht.time: Dedicated Time Series
  m:            Derivative of the heart timing signal,
                  dedicated time series is "ht.time"
  r:            Instantaneous Heart Rate
                  dedicated time series is "ht.time"


 Example:
   [ht,m,r] = ECTBcorr(QRStime_corr,ectb_times);


 Filename: ECTBcorr.m
 Last modified: 2003/09/15
 Copyright (c) 2003 by Johannes Peham

 Reference:
 [1] J. Mateo, P. Laguna, Analysis of Heart Rate Variability in Presence
      of Ectopic Beats Using the Heart Timing Signal
     IEEE Transactions on biomedical engineering,
      Vol.50, No.3, March 2003


 This library is free software; you can redistribute it and/or
 modify it under the terms of the GNU Library General Public
 License as published by the Free Software Foundation; either
 Version 2 of the License, or (at your option) any later version.

 This library 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
 Library General Public License for more details.

 You should have received a copy of the GNU Library General Public
 License along with this library; if not, write to the
 Free Software Foundation, Inc., 59 Temple Place - Suite 330,
 Boston, MA  02111-1307, USA.



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 Correction of ectopic beat Presence Effect

 This Algorithm corrects the e...



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evoked_potential


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 EVOKED_POTENTIAL estimates evoked potentials (EP's)

  R = EVOKED_POTENTIAL(filename, CHAN, t_start, t_end, EventTyp)
  R = EVOKED_POTENTIAL(s, HDR, t_start, t_end, EventTyp)
     filename  filename
     CHAN      channel selection; default: 0 (all)
     t_start   start time in seconds (relative to trigger time point)
     t_end     end time in seconds relative to trigger 
     EventTyp  (list of) trigger events

  The trigger information is obtained from HDR.EVENT.POS(HDR.EVENT.TYP==EventTyp))
  The EP is calculated for each selected channel. If more than one 
  EventTyp is used, an EP is obtained for every channel and every 
  type of events. The result can be visualized with 
     plota(R) 
 
 see also: PLOTA



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 EVOKED_POTENTIAL estimates evoked potentials (EP's)



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get_local_maxima_above_threshold


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 GET_LOCAL_MAXIMA_ABOVE_THRESHOLD is used to identify the events
 derived from the trigger trace. It has been used to identify
 miniature EPSP and IPSP events. The functionality of Mode=0 resembles a
 function implemented in FBrain [1-3].
 This functions is provided for backwards compatibility, for new projects you
 should consider using findpeaks() instead.

 pos = get_local_maxima_above_threshold(data,TH)
 pos = get_local_maxima_above_threshold(data,TH,Mode)
 pos = get_local_maxima_above_threshold(data,TH,Mode,winlen)

 Input:
   data: sample vector of detection trace
   TH: threshold
   Mode==0: [default], detects all (local) maxima above threshold [1]
         1: only one maximum above threshold within the
               window of size winlen is considered [2].
         2: only single detections are considered.
               if two detections with a distance smaller than
               winlen occur, both are omitted. That might be
               useful for optain clean candiate templates.
         3: single detection for period between positive
               and negative threshold crossing (winlen is ignored)
         4: single detection for period between positive
               and negative threshold, however the distance
               winlen must exceed the distance between the
               positive and negative threshold crossing
         5: similar to 4, but use a refractory period of winlen,
               for avoid multiple detections.
               single detection for period between positive
               and negative threshold, however the distance
               winlen must exceed the distance between the
               positive and negative threshold crossing

   winlen (Mode=1 only): window length (in number of samples)
               in which all detections collapse to one event

 Output:
   pos: time points of local maxima above threshold

 see also: signal_deconvolution, findpeaks

 Reference(s): 
 [1] A. Pernía-Andrade, S.P. Goswami, Y. Stickler, U. Fröbe, A. Schlögl, and P. Jonas (2012)
     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.
 [2] Zhang X., Schloegl A., Vandael D., Jonas P. (2020)
      A novel machine learning-based method for accurate and efficient detection of
      subthreshold synaptic events in vivo and in vitro (in revision).
      Journal of Neuroscience Methods.
 [3] Zhang X, Schlögl A, Vandael D, Jonas P (2021),
     MOD: A novel machine-learning optimal-filtering method for accurate and efficient
        detection of subthreshold synaptic events in vivo.
     Journal of Neuroscience Methods, 2021.
     doi:10.1016/j.jneumeth.2021.109125



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 GET_LOCAL_MAXIMA_ABOVE_THRESHOLD is used to identify the events
 derived fro...



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getar0


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 GETAR0 calculates average AR parameters for initialization of 
 AAR estimation

 C0 = getar0(S,P,NTR,NN);
    S  signal 
    P  list of model orders
    NTR  number of realizations
    NN   length each segment
    C0 extended covarance matrix (contains a0 and A0)

 [a0,A0] = getar0(...); % in future this will become obsolete 
    a0 average AR-parameters	
    A0 covariance 



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 GETAR0 calculates average AR parameters for initialization of 
 AAR estimation



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heartratevariability


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 HeartRateVariability Analysis according to [1]

 X = heartratevariability(RRI [,units])
 X = heartratevariability(qrsindex [,Fs])
 X = heartratevariability(HDR)
 X = heartratevariability(HDR.EVENT)
 X = heartratevariability(filename)
 
 
 INPUT
   RRI 	R-R-intervales [in seconds]
   HDR		as defined in the header structure of BioSig 
		and returned by QRS-detection
   filename 	with event information
   Fs 		sampling rate - used for conversion into time axis
   units	time units e.g. 'ms' (default: 's')  

 OUTPUT
   X  		struct containing the results as defined by [1]
   X.meanNN      	meanRR = meanNN
   X.SDNN		standard deviation of RR intervales
   X.RMSSD       	rmsSD = SDSD
     NN50count1
     NN50count2
     NN50count
	pNN50
	SD1		width of Poincaré plot; equivalent to sqrt(2)*RMSSD [2]
	SD2		length of Poincaré plot; i.e. 2SDRR²+SDSD²/2 [2]
	r_RR 		correlation coefficient [2]
        AR-based spectral estimation 
   X.VLF               power of very low frequency band (< 0.04 Hz) 
   X.LF                power of low frequency band (0.04-0.15 Hz)
   X.HF                power of high low frequency band (0.15-0.4 Hz)
   X.TotalPower        total power 
   X.LFHFratio         LF/HF-ratio
   X.LFnu              normalized units of LF power (0.04-0.15 Hz)
   X.HFnu              normalized units of HF power  (0.15-0.4 Hz)
        FFT-based spectral estimations 
   X.FFT.VLF
   X.FFT.LF
   X.FFT.HF
   X.FFT.TotalPower
   X.FFT.LFHFratio
   X.FFT.LFnu
   X.FFT.HFnu

  semilogy(X.f,X.ASpectrum) shows the spectral density function
  semilogy(X.FFT.f,X.FFT.ASpectrum) shows the FFT-based spectral density function

 The spectral estimates are based on an autoregressive spectrum estimator 
 of the data which is oversampled by a factor of 4 using the Berger method.  
 The default model order is 15. In order to change these default settings, 
 change in the source code line 211 (oversampling factor) and/or 
 line 230 (order of the autoregressive model); 

 see also: QRSDETECT, BERGER, EVENTCODES.TXT

 Reference(s):
 [1] Heart Rate Variability
       Standards of Measurement, physilogcial interpretation and clinical use.  
       Taskforce of the European Society for Cardiology and the North Americal Society of Pacing and Electrophysiology.         
       European Heart Journal (1996) 17, 354-381. 
 [2] M. Brennan, M.Palaniswami, P. Kamen
	Do Existing Measures of Poincaré Plot Geometriy Reflect Nonlinear Features of Heart Rate Variablilty?
	IEEE Trans Biomedical Eng. 48(11),2001, 
 [3] U. Rajendra Acharya, K. Paul Joseph,N. Kannathal, Choo Min Lim, Jasjit S. Suri. 
	Heart rate variability: a review.
	Med Bio Eng Comput (2006) 44:1031-1051



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 HeartRateVariability Analysis according to [1]



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hjorth


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undocumented function: [ACTIVITY, MOBILITY, COMPLEXITY, m0, m1, m2] = hjorth (S, UC, A)


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undocumented function: [ACTIVITY, MOBILITY, COMPLEXITY, m0, m1, m2] = hjorth ...



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lumped


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 Transfer functions of the lumped alpha model
 [B,A]=lumped(K [,Fs])

  [B,A] are the denominator and nominator, resp. , for a 
       the coupling parameter K and the sampling frequency Fs [default 128Hz].

 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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 Transfer functions of the lumped alpha model
 [B,A]=lumped(K [,Fs])



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nqrsdetect


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 nqrsdetect - detection of QRS-complexes

   QRS=nqrsdetect(S,fs);

 INPUT
   S       ecg signal data
   fs      sample rate

 OUTPUT
   QRS     fiducial points of qrs complexes


 see also: QRSDETECT

 Reference(s):
 [1]: V. Afonso, W. Tompkins, T. Nguyen, and S. Luo, "ECG beat detection using filter banks,"
 	IEEE Trans. Biomed. Eng., vol. 46, no. 2, pp. 192--202, Feb. 1999
 [2]: A.V. Oppenheim, R.W. Schafer, and J.R. Buck,  Discrete-Time Signal
 	Processing, second edition, Prentice Hall, 1999, chapter 4.7.3



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 nqrsdetect - detection of QRS-complexes



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oahe


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 OAHE detectes obstructive Apnea/Hypopnea event

    [Y,EVENT] = OAHE(X,Fs)
    [Y,EVENT] = OAHE(filename,CHAN)
   ... = OAHE(... ,'-o',outputFilename)
   ... = OAHE(... ,'-e',eventFilename)

 INPUT:
       X   respiratory channel
       Fs  sampleing rate
       filename        source filename 
       CHAN            respiratory channels for calculating OAHE
	outputFilename
		name of file for storing the resulting data with the
		detected spikes and bursts in GDF format.
	eventFilename
		filename to store event inforamation in GDF format. this is similar to 
		the outputFile, except that the signal data is not included and is, therefore,
		much smaller than the outputFile

 OUTPUT: 
       Y       detection trace
       EVENT   event structure as used in BIOSIG  

 see also: SVIEWER, SLOAD 


 REFERENCES:
 [1] AASM Task Force. Sleep-Related Breathing Disorders in Adults: 
       Recommendations for Syndrome Definition, and Measurement Techniques in Clinical Research. Sleep, 22(5), 1999. 
 [2] Meoli AL, Casey KR, Clark RW, Coleman JA Jr, Fayle RW, Troell RJ, Iber C; Clinical Practice Review Committee. 
       Hypopnea in Sleep-Disordered Breathing in Adults. Sleep. 2001 Jun 15;24(4):469-70.
 [3] Penzel T.,  Brandenburg U., Fischer J., Jobert M., Kurella B., Mayer G., Nioewerth H.J., Peter J.H., Pollmächer T., Schäfer T., Steinberg R., Trowitzsch E., Warmuth R., Weeß H.-G., Wölk C., Zulley J., 
       Empfehlungen zur computerunterstützen Aufzeichnung und Auswertung von Polygraphien. Somnologie, 2, 42-48, 1998. 
 [4] Ross SD, Sheinhait IA, Harrison KJ, Kvasz M, Connelly JE, Shea SA, Allen IE. 
       Systematic review and meta-analysis of the literature regarding the diagnosis of sleep apnea. Sleep. 2000 Jun 15;23(4):519-32.
 [5] Schafer T. Methodik der Atmungsmessung im Schlaf: Kapneographie zur Beurteilung der Ventilation. 
       Biomed Tech (Berl). 2003 Jun; 48(6):170-5.
 [6] Thurnheer R, Xie X, Bloch KE. Accuracy of nasal cannula pressure recordings for assessment of ventilation during sleep. 
       Am J Respir Crit Care Med. 2001 Nov 15;164(10 Pt 1):1914-9.
 [7] Whitney CW, Gottlieb DJ, Redline S, Norman RG, Dodge RR, Shahar E, Surovec S, Nieto FJ. 
       Reliability of scoring respiratory disturbance indices and sleep staging. Sleep. 1998 Nov 1;21(7):749-57.
 [8] Schlögl A, Kemp B, Penzel T, Kunz D, Himanen SL, Varri A, Dorffner G, Pfurtscheller 
       G. Quality control of polysomnographic sleep data by histogram and entropy analysis.
       Clin Neurophysiol. 1999 Dec;110(12):2165-70.



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 OAHE detectes obstructive Apnea/Hypopnea event



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paynter


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 PAYNTER returns the filter coefficients for a Paynter Filter
   Usually, the filter is applied to the rectified electromyogram (EMG).
   Then, the output is amplitude-demodulated EMG 

  The amplitude demodulated EMG can be obtained through
      y = filter(B,A,abs(x));
   with 
       [B,A]=paynter(tau,fs)
       [B,A]=paynter(tau,fs,'modified')
       [B,A]=paynter(tau,fs,'bessel-modified')

       tau	time constant (in [s])
       fs	sampling rate (in [Hz])


 REFERENCE(S):
 [1] Platt, Ronald S., Eric A. Hajduk, Manuel Hulliger, and Paul A. Easton. 
       A modified Bessel filter for amplitude demodulation of respiratory electromyograms. 
       J. Appl. Physiol. 84(1): 378-388, 1998.
       available online:  http://jap.physiology.org/cgi/content/full/84/1/378
 [2] Gottlieb, G.L. and Agarwal. 
       Filtering of Electromyographic Signals. Am.J.Physical Medicine 49(3):142-146, 1970.
 [3] Hopp, F.A., J.L. Seagard, and J.P. Kampine. 
     Comparison of four methods of averaging nerve activity. Am. J. Physiology 251:R700-R711, 1986.
 [4] Bruce, E. N., M. D. Goldman, and J. Mead. 
       A digital computer technique for analyzing respiratory muscle EMGs. 
       J. Appl. Physiol. 43: 551-556, 1977 
       available online:  http://jap.physiology.org/cgi/content/abstract/43/3/551



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   Usually, the...



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processing


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 Data processing in the BIOSIG toolbox, 
 This functions can be used as template as well as a wrapper 
 around different signal processing methods.  

 [Y,Zf] = processing(MODE,X,Zi)

    X input signal 
    Zi input status (optional), 
	initial condition
	if empty or not available, this initializes the method
    Y  output signal	
    Zf final condition 
    MODE is a struct and determines the signal processing method

    MODE =
	{'ECG_envelope',Fs}       used for QRS-detection [1]
	{'ECG_envelope',Fs,Threshold}  used for QRS-detection [1]
    	'xyz'		-none-	


 Reference(s):
 [1] M.-E. Nygards, L. Sörnmo, Delineation of the QRS complex using the envelope of the e.c.g
         Med. & Biol. Eng. & Comput., 1983, 21, 538-547.





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 This functions can be used as templ...



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qrscorr


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 Identification and correction of detection errors on QRS-beat sequences

 This algorithm identifies anomalies like FP, FN or ectopic beats.
  The FP and FN are corrected by deleting or inserting one or multiple beats.

 INPUT:
  QRSindex:     Sample values of the detected QRS-Complexes(minimum (8+(Nmax-1)Samples)
  Fs:           Sample Frequency
  Nmax:         Maximum number of insertions, deletions, movements at one position (default = 20)

 OUTPUT:
  QRStime_corr: Time values of the corrected QRS-Complexes
  dr:           Time values of the derivative of the original instantaneous
                 heart rate:
     dr = 2 * | (t(k-1) - 2t(k) + t(k+1)) / ((t(k-1) - t(k))*(t(k-1) - t(k+1))*(t(k) - t(k+1))) |
  dr_corr:      Time values of the derivative of the corrected instantaneous
                 heart rate:
  U:            Calculated threshold for dr (Umax = 0.5 [s-2])
  ANNOT:        Structure that annotates the corrected and uncorrected beats
  ANNOT:         ANNOT.ins: Time values of all inserted beats
                 ANNOT.del: Time values of all deleted beats
                 ANNOT.mov: Time values of all moved beats
                 ANNOT.NOR: Time values of all uncorrected beats


 [QRStime_corr,ANNOT] = QRScorr(QRSindex,Fs,Nmax);

 Example:
  [QRStime_corr,ANNOT] = QRScorr(QRSindex,256,15);


 Reference:
 [1] J. Mateo, P. Laguna, Analysis of Heart Rate Variability in Presence
      of Ectopic Beats Using the Heart Timing Signal
     IEEE Transactions on biomedical engineering,
      Vol.50, No.3, March 2003



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 Identification and correction of detection errors on QRS-beat sequences

 ...



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qrsdetect


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 QRSDETECT - detection of QRS-complexes

   HDR = qrsdetect(fn,chan,Mode)
   ... = qrsdetect(fn, 0, Mode, '-o',outputFilename)
   ... = qrsdetect(... ,'-e',eventFilename)
   HDR = qrsdetect(s,Fs,Mode) 

 INPUT
   	fn	filename
   	chan    channel number of ecg data
		if chan is empty, all channels which contain ECG, ecg, or EKG in HDR.Label 
		are used. 
   	s       ecg signal data 
   	Fs      sample rate 
   	Mode    optional - default is 2
               1: method [1] is used
		2: method [2] is used
	outputFilename
		name of file for storing the resulting data with the
		detected spikes and bursts in GDF format.
		
	eventFilename
		filename to store event inforamation in GDF format. this is similar to 
		the outputFile, except that the signal data is not included and is, therefore,
		much smaller than the outputFile

 OUTPUT
   HDR.EVENT  fiducial points of qrs complexes	


 see also: PROCESSING, EVENTCODES.TXT, SLOAD 

 Reference(s):
 [1] M.-E. Nygards, L. Sörnmo, Delineation of the QRS complex using the envelope of the e.c.g
       Med. & Biol. Eng. & Comput., 1983, 21, 538-547.
 [2] V. Afonso, W. Tompkins, T. Nguyen, and S. Luo, "ECG beat detection using filter banks."
 	IEEE Trans. Biomed. Eng. 46(2):192-202, Feb. 1999.



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 QRSDETECT - detection of QRS-complexes



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respdetect


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 respdetect - detection of respiration

   r=respdetect(S,fs);

 INPUT
   S       signal data
   fs      sample rate

 OUTPUT
   r       fiducial points of the respiration



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 respdetect - detection of respiration



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signal_deconvolution


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 SIGNAL_DECONVOLUTION deconvolves some raw data with some given template in order
   to improve the detection of miniature epsc's, and ipsc's. 

 d = SIGNAL_DECONVOLUTION(raw,template,samplerate,highpass,lowpass)
 ... SIGNAL_DECONVOLUTION(raw,template,samplerate,[highpass,lowpass])
 ... SIGNAL_DECONVOLUTION(raw,template,samplerate,[lowpass, highpass])

 INPUT:
    raw: raw data (a Nx1 data vector)
    template: template (a Mx1 data vector)
	it is assumed that the template starts immidiately with the first sample 		
    samplerate: sampling rate in Hz
    highpass: edge frequency of highpass filter in Hz, default 0.1 Hz.
    lowpass: edge frequency of lowpass filter in Hz, default 100 Hz. 
 	
 Output: 
    d: detection trace 

 see also: get_local_maxima_above_threshold

 Reference(s): 
  [1] A. Pernía-Andrade, S.P. Goswami, Y. Stickler, U. Fröbe, A. Schlögl, and P. Jonas (2012)
     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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 SIGNAL_DECONVOLUTION deconvolves some raw data with some given template in o...



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synthetic_ecg


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 function ecg = synthetic_ecg(heart_rate,heart_peak,durration,srate)
 heart_rate is the desired frequency in beats per minute, and heart_peak
 describes the R- amplitude of the generated ECG in microvolts.
 "durration" is the durration of the recording in seconds, and finally 
 srate is the sampling rate.



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 function ecg = synthetic_ecg(heart_rate,heart_peak,durration,srate)
 heart_r...



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tdp


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undocumented function: [F, G] = tdp (S, p, UC, A)


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undocumented function: [F, G] = tdp (S, p, UC, A)



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teager


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 TEAGER computes the teager-kaiser operaterion [1]
   
  y = teager(x)
  

 see also: 

 REFERENCE(S):
 [1] J. K. Kaiser, “On a simple algorithm to calculate the energy of
 a signal,” Proc. IEEE ICASSP 90, vol 1, pp. 381-384, 1990.




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 TEAGER computes the teager-kaiser operaterion [1]
   
  y = teager(x)
  



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tfmvar


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 TFMVAR Time-Frequency MVAR analysis
   time-frequency analysis of 
   multivariate stochastic processes. 

 [R] = tfmvar(s,TRIG,T,MOP,f,Fs, [CL,group])
 [R] = tfmvar(s,TRIG,T,MOP,N,Fs, [CL,group])

 INPUT: 
  s    signal data (one channel per column) 
  TRIG trigger time points (in SAMPLES)
  T    windows definition; each column defines one window)
       T(1,:) and T(2,:) indicate start and end [in samples], respectivly  
  MOP  model order of the MVAR model   
  f    vector of designated frequencies
  N 	(scalar) number of frequencies distributed between 0..Fs/2 	
  Fs   sampling rate. 
  [CL,group]  is OPTIONAL
	CL 	are the labels for different classes, conditions, states. 
		CL must be a column vector having the same length than TRIG
       group 	is useful for controlling the resampling
		same numbers indicate that member belongs to the same group. 
		E.g. if data from several subjects are concatanated, and the 
		the trials of each subject have the same numbers, the standard error 
		of the group-statistic is estimated. 
		If group is empty [default], each trial gets a different number; 
		Accordingly, a trial-based leave-on-out-method (LOOM) is used, 
		for computing the standard error. 
               

 OUTPUT: 
     	M and SE contain the mean 
	and the standard error of the mean  
       of the following characteristic parameters. 
       The size of the parameters is defined by the number of channels,
       the number of windows the number of designated
       frequencies [size(s,2), size(T,1), length(f)] respectively. 

 univariate:
   S1		Autospectra
   logS1   	log(abs(S1))
   AR1         univariate autoregressive parameters 
   C1          variance of predication error 

 multivariate: 
   S		Auto- and Cross-spectra
   h		transfer function 		
   logS   	log(abs(S))
   logh   	log(abs(h))
   y1i         imaginary part of amplitude spectra 
   h1i         imaginary part of transfer function
   phaseS   	phase of S
   phaseh   	phase of h
   COH		coherence
   coh		coherence neglecting the cross-correlation 
		  due to the innovation process
   pCOH 	partial coherence
   PDC	 	partial directed coherence [2, 5]
   DTF 	directed transfer function [3, 6]
   dDTF 	modified DTF [8]
   ffDTF 	modified DTF [8]
   AR		MVAR parameters
   C		covariance matrix of the innovation process	
   DC		directed granger causality [2,3,5,6]
   GGC		Geweke's Granger Causality (not quite the same as in [12,13]
   Af		Frequency transform of A(z)

 [R] = tfmvar(s,TRIG,T,MOP,f,Fs)
   R is a struct containing M and SE as well as a few more 
     parameters for visualization

  The standard error is calculated with a jackknife-method,
  based on LEAVE-K-TRIALs-OUT. Therefore, the SE need to be 
  rescaled, depending on the needs [10,11]. 
     SE 
	standard error of the mean from the bootstrap results 
	This has usually no common meaning (pretty much useless). 
     SE*(N-K)^(1/2) 
	standard deviation of the means from the bootstrapping
	It can be interpreted as the standard error of the total mean 
	(across all trials).
	This value becames smaller if the number of trials increase. 	
     SE*(N-K) 	
	average standard error of the mean (based on a single trial).
	This value provides a realistic value for the confidence 
	interval of the estimates and can be used to test the 
	significance. 
     SE*(N-K)*N^(1/2) 
	[estimated] standard deviation of a single trial estimate
	This value is important for a single-trial classification.  
		

 see also: tsa/MVAR, tsa/MVFREQZ, PLOTA

 Reference(s):
 [1] A. Schlögl, G. Supp.Analyzing event-related EEG data with multivariate autoregressive parameters.
      (Eds.) C. Neuper and W. Klimesch, Progress in Brain Research: Event-related Dynamics of Brain Oscillations. 
      Analysis of dynamics of brain oscillations: methodological advances. Elsevier. 
      Progress in Brain Research 159, 2006, p. 135 - 147
 [2] Baccala L. A., Sameshima K., Partial Directed Coherence: A New Concept in Neural Structure Determination, Biological Cybernetics 84, 2001
 [3] Kaminski M., Blinowska K., Szelenberger W., Topographic Analysis of Coherence and Propagation of EEG Activity During Sleep and Wakefulness, Electroencephalography and Clinical Neurophysiology 102, 1997
 [4] Franaszczuk P. J., Bergey G. K., An Autoregressive Method for the Measurement of Synchronization of Interictal and Ictal EEG Signals, Biological Cybernetics 81, 1999
 [5] Sameshima K., Baccala L. A., Using Partial Directed Coherence to Describe Neuronal Ensemble Interactions, Journal of Neuroscience Methods 94, 1999
 [6] Kaminski M., Ding M., Truccolo W. A., Bressler S. L., Evaluating Causal Relations in Neural Systems: Granger Causality, Directed Transfer Function and Statistical Assessment of Significance, Biological Cybernetics 85, 2001
 [7] Liang H., Ding M., Bressler S. L., On the Tracking of Dynamic Functional Relations in Monkey Cerebral Cortex, Neurocomputing, 2000
 [8] Korzeniewska A., Manczak M., Kaminski M., Blinowska K. J., Kasicki S., Determination of Information Flow Direction Among Brain Structures By a Modified Directed Transfer Function (dDTF) Method, Journal of Neuroscience Methods 125, 2003
 [9] A. Schl\"ogl, Comparison of Multivariate Autoregressive Estimators. Signal processing, Elsevier B.V. (in press). 
       available at http://dx.doi.org/doi:10.1016/j.sigpro.2005.11.007
 [10] http://www.physics.utah.edu/~detar/phycs6730/handouts/jackknife/jackknife/jackknife.html
 [11] http://www-stat.stanford.edu/~susan/courses/s208/node16.html
 [12] Geweke J., 1982. J.Am.Stat.Assoc., 77, 304-313.
 [13] Bressler S.L., Richter C.G., Chen Y., Ding M. (2007)
	Cortical fuctional network organization from autoregressive modelling of loal field potential oscillations.
	Statistics in Medicine, doi: 10.1002/sim.2935 



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 TFMVAR Time-Frequency MVAR analysis
   time-frequency analysis of 
   multiv...



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tvaar


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 TVAAR wrapper around adaptive autoregressive estimator. 
       X = tvaar(signal,p,UC)
       X = tvaar(signal,X)
  
 INPUT:
   X.MOP=[d,p,q]
       d = 1: with mean term; d=0: without mean term
       p:    order of AutoRegressive part 
       q:    order of Moving Average
   X.UC  update coefficient
   X.Mode = [amode,vmode]: choose estimation algorithm    
   X.Z0  covariance of state estimates
   X.z0  initial state vector 
   X.W0  covariance of system noise 
   X.V0  variance of observation noise

 OUTPUT:
   X.Z0  average covariance of state estimates
   X.z0  average state vector 
   X.W0  covariance of system noise 
   X.V0  variance of prediction error
   X.AAR estimated AAR parameters
   X.E   prediction error, residuum, 
   X.PE  time-varying variance of residual process
 
 REFERENCES: 
 [1] Schlögl A.(2000)
   The electroencephalogram and the adaptive autoregressive model: theory and applications
   Shaker Verlag, Aachen, Germany,(ISBN3-8265-7640-3). 
 [2] Schlögl A, Lee FY, Bischof H, Pfurtscheller G
   Characterization of Four-Class Motor Imagery EEG Data for the BCI-Competition 2005.
   Journal of neural engineering 2 (2005) 4, S. L14-L22



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 TVAAR wrapper around adaptive autoregressive estimator.



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wackermann


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undocumented function: [SIGMA, PHI, OMEGA, m0, m1, m2, cout] = wackermann (S, UC, A, cini)


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undocumented function: [SIGMA, PHI, OMEGA, m0, m1, m2, cout] = wackermann (S,...





