P300 Detection Based on EEG Shape Features

We present a novel approach to describe a P300 by a shape-feature vector, which offers several advantages over the feature vector used by the BCI2000 system. Additionally, we present a calibration algorithm that reduces the dimensionality of the shape-feature vector, the number of trials, and the electrodes needed by a Brain Computer Interface to accurately detect P300s; we also define a method to find a template that best represents, for a given electrode, the subject's P300 based on his/her own acquired signals. Our experiments with 21 subjects showed that the SWLDA's performance using our shape-feature vector was 93%, that is, 10% higher than the one obtained with BCI2000-feature's vector. The shape-feature vector is 34-dimensional for every electrode; however, it is possible to significantly reduce its dimensionality while keeping a high sensitivity. The validation of the calibration algorithm showed an averaged area under the ROC (AUROC) curve of 0.88. Also, most of the subjects needed less than 15 trials to have an AUROC superior to 0.8. Finally, we found that the electrode C4 also leads to better classification.

[1]  Borut Zalik,et al.  An efficient chain code with Huffman coding , 2005, Pattern Recognit..

[2]  S. Cerutti,et al.  Dynamic time warping in the analysis of event-related potentials , 2005, IEEE Engineering in Medicine and Biology Magazine.

[3]  T.M. Vaughan,et al.  Common Spatio-Temporal Patterns for the P300 Speller , 2007, 2007 3rd International IEEE/EMBS Conference on Neural Engineering.

[4]  Ernesto Bribiesca,et al.  A measure of tortuosity based on chain coding , 2013, Pattern Recognit..

[5]  Wendy Aguilar,et al.  A Measure of Shape Dissimilarity for 3D Curves , 2006 .

[6]  P. Jaskowski,et al.  Amplitudes and latencies of single-trial ERP's estimated by a maximum-likelihood method , 1999, IEEE Transactions on Biomedical Engineering.

[7]  E Donchin,et al.  The mental prosthesis: assessing the speed of a P300-based brain-computer interface. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[8]  J. Polich,et al.  Neuropsychology and neuropharmacology of P3a and P3b. , 2006, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[9]  Mingzhou Ding,et al.  Differentially Variable Component Analysis (dVCA): Identifying Multiple Evoked Components using Trial-to-Trial Variability , 2003 .

[10]  Laurent Bougrain,et al.  An Open-Access P300 Speller Database , 2010 .

[11]  B.M. Oliver,et al.  The Philosophy of PCM , 1948, Proceedings of the IRE.

[12]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[13]  Dean J. Krusienski,et al.  Amplitude quantization of event related potentials for brain-computer interfaces , 2009, 2009 4th International IEEE/EMBS Conference on Neural Engineering.

[14]  Kongming Wang,et al.  Warp-averaging event-related potentials , 2001, Clinical Neurophysiology.

[15]  Wim van Drongelen,et al.  Signal processing for Neuroscientists : introduction to the analysis of physiological signals , 2007 .

[16]  Hiroyuki Kambara,et al.  A comparison of dimensionality reduction techniques for the P300 response , 2009, i-CREATe.

[17]  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.

[18]  David A. Rosenblueth,et al.  Definite-clause grammars for 2D shape analysis , 1995 .

[19]  C. Woody.  Characterization of an adaptive filter for the analysis of variable latency neuroelectric signals , 1967, Medical and biological engineering.

[20]  S. Coyle,et al.  Brain–computer interfaces: a review , 2003 .

[21]  Martti Juhola,et al.  Syntactic recognition of ECG signals by attributed finite automata , 1995, Pattern Recognit..

[22]  Cuntai Guan,et al.  High performance P300 speller for brain-computer interface , 2004, IEEE International Workshop on Biomedical Circuits and Systems, 2004..

[23]  N. Draper,et al.  Applied Regression Analysis: Draper/Applied Regression Analysis , 1998 .

[24]  E. W. Sellers,et al.  Toward enhanced P300 speller performance , 2008, Journal of Neuroscience Methods.

[25]  J. Polich,et al.  Cognitive and biological determinants of P300: an integrative review , 1995, Biological Psychology.

[26]  Dean J Krusienski,et al.  A comparison of classification techniques for the P300 Speller , 2006, Journal of neural engineering.

[27]  R. Verleger,et al.  An evaluation of methods for single-trial estimation of P3 latency. , 2000, Psychophysiology.

[28]  Alejandro Blumenkrans,et al.  Two-dimensional object recognition using a two-dimensional polar transform , 1991, Pattern Recognit..

[29]  Laurent Bougrain,et al.  Averaging techniques for single-trial analysis of oddball event-related potentials , 2008 .

[30]  Urbano Nunes,et al.  Statistical spatial filtering for a P300-based BCI: Tests in able-bodied, and patients with cerebral palsy and amyotrophic lateral sclerosis , 2011, Journal of Neuroscience Methods.

[31]  Ernesto Bribiesca,et al.  A Geometric structure for two-dimensional shapes and three-dimensional surfaces , 1992, Pattern Recognit..

[32]  T. Picton,et al.  Evaluation of brain-stem auditory evoked potentials using dynamic time warping. , 1988, Electroencephalography and clinical neurophysiology.

[33]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[34]  I. P. Ganin,et al.  A P300-based Brain-Computer Interface with Stimuli on Moving Objects: Four-Session Single-Trial and Triple-Trial Tests with a Game-Like Task Design , 2013, PloS one.

[35]  Alain Rakotomamonjy,et al.  BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller , 2008, IEEE Transactions on Biomedical Engineering.

[36]  O. Yanez-Suarez,et al.  Evoked Potentials SNR maximization by PCA and genetic algorithms , 2011, 2011 5th International IEEE/EMBS Conference on Neural Engineering.

[37]  C. Neuper,et al.  Toward a high-throughput auditory P300-based brain–computer interface , 2009, Clinical Neurophysiology.

[38]  Hermilo Sánchez-Cruz,et al.  Compressing bilevel images by means of a three-bit chain code , 2005 .

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

[40]  J. A. C. Weideman,et al.  Numerical Integration of Periodic Functions: A Few Examples , 2002, Am. Math. Mon..

[41]  John R. Bourne,et al.  Syntactic Analysis of the Electroencephalogram , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[42]  Vladimir Bostanov,et al.  BCI competition 2003-data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram , 2004, IEEE Transactions on Biomedical Engineering.

[43]  Richard F. Gunst,et al.  Applied Regression Analysis , 1999, Technometrics.

[44]  Helge J. Ritter,et al.  BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm , 2004, IEEE Transactions on Biomedical Engineering.

[45]  Yanina Atum,et al.  Genetic feature selection to optimally detect P300 in brain computer interfaces , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[46]  Ernesto Bribiesca,et al.  A new chain code , 1999, Pattern Recognit..

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