AutoRegressive Coefficients


  • Plugin name : AutoRegressive Coefficients
  • Version : 1.0
  • Author : Alison Cellard
  • Company : Inria
  • Short description : Estimates autoregressive (AR) coefficients from a set of signals
  • Documentation template generation date : Apr 11 2018


Estimates autoregressive (AR) linear model coefficients using Burg's method

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 :


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

For more informations about AR model :

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


1. EEG Signal

The input signal

  • Type identifier : Signal (0x5ba36127, 0x195feae1)


1. AR Features

The AR coefficients stored in a Feature vector

  • Type identifier : Streamed matrix (0x544a003e, 0x6dcba5f6)


1. Order

Specify the order, thus the number of coefficients calculated

  • Type identifier : Integer (0x007deef9, 0x2f3e95c6)
  • Default value : [ 1 ]



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