P300-Brain Computer Interface Based on Ordinal Analysis of Time Series

A brain computer interface (BCI) is a novel communication system that translates brain signals into control commands. In this paper, we present a P300 BCI system based on ordinal pattern features. Compared to BCI system based on linear time domain features, we have shown that slightly better classification accuracies and bitrates can be achieved for healthy and disabled subjects.

[1]  Nicholas Rohrbacker Analysis of Electroencephologram Data Using Time-Delay Embeddings to Reconstruct Phase Space , 2009 .

[2]  Norbert Marwan,et al.  Extended Recurrence Plot Analysis and its Application to ERP Data , 2002, Int. J. Bifurc. Chaos.

[3]  L M Hively,et al.  Detecting dynamical changes in time series using the permutation entropy. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Gaoxiang Ouyang,et al.  Ordinal pattern based similarity analysis for EEG recordings , 2010, Clinical Neurophysiology.

[5]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

[6]  Jürgen Kurths,et al.  Recurrence plots for the analysis of complex systems , 2009 .

[7]  C. Stam,et al.  Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field , 2005, Clinical Neurophysiology.

[8]  Katharina Wittfeld,et al.  Distances of Time Series Components by Means of Symbolic Dynamics , 2004, Int. J. Bifurc. Chaos.

[9]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[10]  J. Rogers Chaos , 1876 .

[11]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[12]  Melody Moore Jackson,et al.  Assessing Fit of Nontraditional Assistive Technologies , 2010, TACC.

[13]  F. Takens Detecting strange attractors in turbulence , 1981 .

[14]  Karsten Keller,et al.  Symbolic Analysis of High-Dimensional Time Series , 2003, Int. J. Bifurc. Chaos.

[15]  C. Bandt Ordinal time series analysis , 2005 .

[16]  Mehmet Emre Çek,et al.  Analysis of observed chaotic data , 2004 .

[17]  Michael Tangermann,et al.  Feature selection for brain-computer interfaces , 2007 .

[18]  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.

[19]  Schwartz,et al.  Singular-value decomposition and the Grassberger-Procaccia algorithm. , 1988, Physical review. A, General physics.

[20]  G. Keller,et al.  Entropy of interval maps via permutations , 2002 .

[21]  J. Kurths,et al.  Spurious Structures in Recurrence Plots Induced by Embedding , 2006 .

[22]  Touradj Ebrahimi,et al.  An efficient P300-based brain–computer interface for disabled subjects , 2008, Journal of Neuroscience Methods.

[23]  Touradj Ebrahimi,et al.  Implicit emotional tagging of multimedia using EEG signals and brain computer interface , 2009, WSM@MM.

[24]  G. Lightbody,et al.  Chaos theory analysis of the newborn EEG - is it worth the wait? , 2005, IEEE International Workshop on Intelligent Signal Processing, 2005..

[25]  J. A. Stewart,et al.  Nonlinear Time Series Analysis , 2015 .

[26]  Han Chongzhao,et al.  Selection of Embedding Dimension and Delay Time in Phase Space Reconstruction , 2006 .

[27]  Ulrich Hoffmann,et al.  Bayesian machine learning applied in a brain-computer interface for disabled users , 2007 .

[28]  Chstoph Bandt,et al.  Order Patterns in Time Series , 2007 .