Natural language processing with dynamic classification improves P300 speller accuracy and bit rate

The P300 speller is an example of a brain-computer interface that can restore functionality to victims of neuromuscular disorders. Although the most common application of this system has been communicating language, the properties and constraints of the linguistic domain have not to date been exploited when decoding brain signals that pertain to language. We hypothesized that combining the standard stepwise linear discriminant analysis with a Naive Bayes classifier and a trigram language model would increase the speed and accuracy of typing with the P300 speller. With integration of natural language processing, we observed significant improvements in accuracy and 40-60% increases in bit rate for all six subjects in a pilot study. This study suggests that integrating information about the linguistic domain can significantly improve signal classification.

[1]  David G. Stork,et al.  Pattern Classification , 1973 .

[2]  J. Wolpaw,et al.  A novel P300-based brain–computer interface stimulus presentation paradigm: Moving beyond rows and columns , 2010, Clinical Neurophysiology.

[3]  Masud Mansuripur,et al.  Introduction to information theory , 1986 .

[4]  J. Wolpaw,et al.  A P300 event-related potential brain–computer interface (BCI): The effects of matrix size and inter stimulus interval on performance , 2006, Biological Psychology.

[5]  G. R. Muller,et al.  Brain oscillations control hand orthosis in a tetraplegic , 2000, Neuroscience Letters.

[6]  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.

[7]  Xingyu Wang,et al.  A new P300 stimulus presentation pattern for EEG-based spelling systems , 2010, Biomedizinische Technik. Biomedical engineering.

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

[9]  Luca Citi,et al.  Documenting, modelling and exploiting P300 amplitude changes due to variable target delays in Donchin's speller , 2010, Journal of neural engineering.

[10]  N. Draper,et al.  Applied Regression Analysis , 1966 .

[11]  Hinrich Schütze,et al.  Book Reviews: Foundations of Statistical Natural Language Processing , 1999, CL.

[12]  Peter Fischer,et al.  The self-assembly, aggregation and phase transitions of food protein systems in one, two and three dimensions , 2013, Reports on progress in physics. Physical Society.

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

[14]  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.

[15]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[16]  Norman R. Draper,et al.  Applied regression analysis (2. ed.) , 1981, Wiley series in probability and mathematical statistics.

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

[18]  G.F. Inbar,et al.  An improved P300-based brain-computer interface , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  Fusheng Yang,et al.  BCI competition 2003-data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications , 2004, IEEE Transactions on Biomedical Engineering.

[20]  Fazlollah M. Reza,et al.  Introduction to Information Theory , 2004, Lecture Notes in Electrical Engineering.

[21]  Frederick Jelinek,et al.  Statistical methods for speech recognition , 1997 .

[22]  F. Massey.  The Kolmogorov-Smirnov Test for Goodness of Fit , 1951 .

[23]  Eric W. Sellers,et al.  Predictive Spelling With a P300-Based Brain–Computer Interface: Increasing the Rate of Communication , 2010, Int. J. Hum. Comput. Interact..

[24]  P H Peckham,et al.  Applications of cortical signals to neuroprosthetic control: a critical review. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[25]  Dennis J. McFarland,et al.  The P300-based brain–computer interface (BCI): Effects of stimulus rate , 2011, Clinical Neurophysiology.