/**
 * \page BoxAlgorithm_AutoRegressiveCoefficients AR Features
__________________________________________________________________

Detailed description
__________________________________________________________________

 * |OVP_DocBegin_BoxAlgorithm_AutoRegressiveCoefficients_Description|
 *The AR features box calculate the coefficients using Burg's method [1] to compute the AutoRegressive (AR) model of an input signal. 
 The AR model is a representation that describes a time varying process by its own previous values. 
 
 The definition used is :   
 \image html ARBurg_Formula.png 
 
 <!-- "Formula of the AR model is x(t)=\sum_{i=1}^N a(i)x(t-i)+e(t)" -->

 Where \e a(i) are the autoregressive coefficients or parameters of the model, \e x(t) is the input signal, \e x(t-i) its previous values, \e N is the order (length) of the model and \e epsilon(t) is the residue, assumed to be Gaussian white noise.

 For more informations about AR model : 
 
 https://en.wikipedia.org/wiki/Autoregressive_model
 
 http://paulbourke.net/miscellaneous/ar/
 
 The model order (see [2]) needs to be specified in the settings of the box.
 
 
 [1] Burg, J.P. (1967) "Maximum Entropy Spectral Analysis", Proceedings of the 37th Meeting of the Society of Exploration Geophysicists, Oklahoma City, Oklahoma
 
 [2] D.J. Krusienski, D.J. MacFarland, J.R. Wolpaw. An evaluation of autoregressive spectral estimation model order for brain-computer interface application. Proceedings of the 28th IEEE EMBS Annual International Conference, New York City, USA, Aug 30-Sept 3, 2006

 * |OVP_DocEnd_BoxAlgorithm_AutoRegressiveCoefficients_Description|
__________________________________________________________________

Inputs description
__________________________________________________________________

 * |OVP_DocBegin_BoxAlgorithm_AutoRegressiveCoefficients_Inputs|
 * |OVP_DocEnd_BoxAlgorithm_AutoRegressiveCoefficients_Inputs|

 * |OVP_DocBegin_BoxAlgorithm_AutoRegressiveCoefficients_Input1|
 The input signal
 * |OVP_DocEnd_BoxAlgorithm_AutoRegressiveCoefficients_Input1|
__________________________________________________________________

Outputs description
__________________________________________________________________

 * |OVP_DocBegin_BoxAlgorithm_AutoRegressiveCoefficients_Outputs|
 * |OVP_DocEnd_BoxAlgorithm_AutoRegressiveCoefficients_Outputs|

 * |OVP_DocBegin_BoxAlgorithm_AutoRegressiveCoefficients_Output1|
 The AR coefficients stored in a Feature vector
 * |OVP_DocEnd_BoxAlgorithm_AutoRegressiveCoefficients_Output1|
__________________________________________________________________

Settings description
__________________________________________________________________

 * |OVP_DocBegin_BoxAlgorithm_AutoRegressiveCoefficients_Settings|
 * |OVP_DocEnd_BoxAlgorithm_AutoRegressiveCoefficients_Settings|

 * |OVP_DocBegin_BoxAlgorithm_AutoRegressiveCoefficients_Setting1|
 Specify the order, thus the number of coefficients calculated
 * |OVP_DocEnd_BoxAlgorithm_AutoRegressiveCoefficients_Setting1|

 * |OVP_DocBegin_BoxAlgorithm_AutoRegressiveCoefficients_Setting2|
 Size of PSD estimation
 * |OVP_DocEnd_BoxAlgorithm_AutoRegressiveCoefficients_Setting2|

 * |OVP_DocBegin_BoxAlgorithm_AutoRegressiveCoefficients_Setting3|
 Option to remove DC component from signal.
 * |OVP_DocEnd_BoxAlgorithm_AutoRegressiveCoefficients_Setting3|
__________________________________________________________________

Examples description
__________________________________________________________________

 * |OVP_DocBegin_BoxAlgorithm_AutoRegressiveCoefficients_Examples|
 * |OVP_DocEnd_BoxAlgorithm_AutoRegressiveCoefficients_Examples|
__________________________________________________________________

Miscellaneous description
__________________________________________________________________

 * |OVP_DocBegin_BoxAlgorithm_AutoRegressiveCoefficients_Miscellaneous|
The output feature vector contains the coefficients for each channel : the first [order+1] elements are the coefficients of the first channel, etc. 
 * |OVP_DocEnd_BoxAlgorithm_AutoRegressiveCoefficients_Miscellaneous|
 */
