Sensorimotor rhythm-based brain–computer interface (BCI): model order selection for autoregressive spectral analysis

People can learn to control EEG features consisting of sensorimotor rhythm amplitudes and can use this control to move a cursor in one or two dimensions to a target on a screen. Cursor movement depends on the estimate of the amplitudes of sensorimotor rhythms. Autoregressive models are often used to provide these estimates. The order of the autoregressive model has varied widely among studies. Through analyses of both simulated and actual EEG data, the present study examines the effects of model order on sensorimotor rhythm measurements and BCI performance. The results show that resolution of lower frequency signals requires higher model orders and that this requirement reflects the temporal span of the model coefficients. This is true for both simulated EEG data and actual EEG data during brain-computer interface (BCI) operation. Increasing model order, and decimating the signal were similarly effective in increasing spectral resolution. Furthermore, for BCI control of two-dimensional cursor movement, higher model orders produced better performance in each dimension and greater independence between horizontal and vertical movements. In sum, these results show that autoregressive model order selection is an important determinant of BCI performance and should be based on criteria that reflect system performance.

[1]  Jonathan R Wolpaw,et al.  Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[2]  D J McFarland,et al.  An EEG-based brain-computer interface for cursor control. , 1991, Electroencephalography and clinical neurophysiology.

[3]  Y. Selen,et al.  Model-order selection: a review of information criterion rules , 2004, IEEE Signal Processing Magazine.

[4]  G Florian,et al.  Dynamic spectral analysis of event-related EEG data. , 1995, Electroencephalography and clinical neurophysiology.

[5]  P. de Chazal,et al.  Parametric models and spectral analysis for classification in brain-computer interfaces , 2002, 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628).

[6]  D.J. McFarland,et al.  Sensorimotor rhythm-based brain-computer interface (BCI): feature selection by regression improves performance , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[8]  Dennis J. McFarland,et al.  Design and operation of an EEG-based brain-computer interface with digital signal processing technology , 1997 .

[9]  Gert Pfurtscheller,et al.  Brain-computer interface: a new communication device for handicapped persons , 1993 .

[10]  H. Lüders,et al.  American Electroencephalographic Society Guidelines for Standard Electrode Position Nomenclature , 1991, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[11]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[12]  Dennis J. McFarland,et al.  Electroencephalographic(EEG)-based communication: EEG control versus system performance in humans , 2003, Neuroscience Letters.

[13]  Gert Pfurtscheller,et al.  Characterization of four-class motor imagery EEG data for the BCI-competition 2005 , 2005, Journal of neural engineering.

[14]  J. Wolpaw,et al.  Multichannel EEG-based brain-computer communication. , 1994, Electroencephalography and clinical neurophysiology.

[15]  H. Flor,et al.  A spelling device for the paralysed , 1999, Nature.

[16]  J R Wolpaw,et al.  Spatial filter selection for EEG-based communication. , 1997, Electroencephalography and clinical neurophysiology.

[17]  H. Akaike.  A new look at the statistical model identification , 1974 .

[18]  B. Porat,et al.  Digital Spectral Analysis with Applications. , 1988 .

[19]  I. A. Kieseppä.  Statistical Model Selection Criteria and the Philosophical Problem of Underdetermination , 2001, The British Journal for the Philosophy of Science.

[20]  Anthony G. Constantinides,et al.  Power spectrum estimation from noisy and limited autocorrelation values: A maximum entropy approach , 1997, Signal Process..

[21]  E. Donchin,et al.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.

[22]  F. Babiloni,et al.  Autoregressive spectral analysis in Brain Computer Interface context , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  Dean J Krusienski,et al.  Brain-computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms. , 2006, Progress in brain research.

[24]  G. Pfurtscheller,et al.  Conversion of EEG activity into cursor movement by a brain-computer interface (BCI) , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[25]  N. Birbaumer,et al.  BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.

[26]  Nuri Firat Ince,et al.  Classification of single trial motor imagery EEG recordings with subject adapted non-dyadic arbitrary time–frequency tilings , 2006, Journal of neural engineering.

[27]  D.J. McFarland,et al.  An Evaluation of Autoregressive Spectral Estimation Model Order for Brain-Computer Interface Applications , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.