wackermann

 Wackermann calculates the global field strength SIGMA,  the global
    frequency PHI and a measure of spatial complexity OMEGA [1]. 
    A stationary and a time-varying (adaptive) estimator is implemented 
   
  [SIGMA,PHI,OMEGA] = wackermann(...)
  
  [...] = wackermann(S,0)
       calculates stationary Wackermann parameter 
  [...] = wackermann(S,UC) with 0<UC<1,
       calculates time-varying Wackermann parameter using 
       exponential window 
  [...] = wackermann(S,N) with N>1,
       calculates time-varying Wackermann parameter using 
       rectangulare window of length N
  [...] = wackermann(S,B,A) with B>=1 oder length(B)>1,
       calulates time-varying Wackermann parameters using 
       transfer function B(z)/A(z) for windowing 

       S       data (each channel is a column)
       UC      update coefficient 
       B,A     filter coefficients (window function) 

 Remark: estimating of Omega requires the eigenvalues, the adaptive estimator  
   utilized the adaptive eigenanalysis method [2].   

 see also: TDP, BARLOW, HJORTH

 REFERENCE(S):
 [1] Jiri Wackermann, Towards a quantitative characterization of
     functional states of the brain: from the non-linear methodology to the
     global linear descriptor. International Journal of Psychophysiology, 34 (1999) 65-80.
 [2] Bin Yang, Projection approximation subspace tracking.
     IEEE Trans. on Signal processing, vol. 43, no. 1 jan. 2005, pp. 95-107.
 [3] Saito N., Kuginuki T., Yagyu T., Kinoshita T., Koenig T., Pascual-Marqui
    R. D., Kochi K., Wackermann J., and Lemann D., 1998.
    Global, regional and local measures of complexity of multichannel EEG in acute, neurolepticnaive,
    first-break schizophrenics. Society of Biological Psychiatry. 43:794–802.