Hangman BCI: An unsupervised adaptive self-paced Brain-Computer Interface for playing games

This paper presents a novel user interface suitable for adaptive Brain Computer Interface (BCI) system. A customized self-paced BCI architecture is introduced where the system combines onset detection system along with an adaptive classifier working in parallel. An unsupervised adaptive method based on sequential expectation maximization for Gaussian mixture model is employed with new timing scheme and an additional averaging step to avoid over-fitting. Sigmoid function based post-processing approach is proposed to enhance the classifiers' output. The adaptive system is compared to a non-adaptive one and tested on five subjects who used the BCI to play the hangman game. The results show significant improvement of the True-False difference for all the classes and a reduction in the number of steps required to solve the problem.

[1]  Stephen J. Roberts,et al.  Sequential Bayesian estimation for adaptive classification , 2008, 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.

[2]  John Q. Gan,et al.  Sequential EM for Unsupervised Adaptive Gaussian Mixture Model Based Classifier , 2009, MLDM.

[3]  José del R. Millán,et al.  Noninvasive brain-actuated control of a mobile robot by human EEG , 2004, IEEE Transactions on Biomedical Engineering.

[4]  G. Pfurtscheller,et al.  Continuous EEG classification during motor imagery-simulation of an asynchronous BCI , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[5]  Geoffrey E. Hinton,et al.  A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.

[6]  Niels Birbaumer,et al.  fMRI Brain-Computer Interface: A Tool for Neuroscientific Research and Treatment , 2007, Comput. Intell. Neurosci..

[7]  Shin Ishii,et al.  On-line EM Algorithm for the Normalized Gaussian Network , 2000, Neural Computation.

[8]  N. Birbaumer,et al.  The thought-translation device (TTD): neurobehavioral mechanisms and clinical outcome , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  Rajesh P. N. Rao,et al.  Towards adaptive classification for BCI , 2006, Journal of neural engineering.

[10]  G Pfurtscheller,et al.  Current trends in Graz Brain-Computer Interface (BCI) research. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[11]  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.

[12]  John Q. Gan,et al.  Unsupervised adaptive GMM for BCI , 2009, 2009 4th International IEEE/EMBS Conference on Neural Engineering.

[13]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[14]  Christa Neuper,et al.  14 Human Brain-Computer Interface , 2005 .

[15]  Stephen J. Roberts,et al.  A self-paced brain–computer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman filter based algorithm for online training , 2009, Medical & Biological Engineering & Computing.

[16]  Gernot R. Müller-Putz,et al.  Self-Paced (Asynchronous) BCI Control of a Wheelchair in Virtual Environments: A Case Study with a Tetraplegic , 2007, Comput. Intell. Neurosci..

[17]  A. Riehle,et al.  Motor Cortex in Voluntary Movements , 2004 .

[18]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[19]  W. A. Sarnacki,et al.  Electroencephalographic (EEG) control of three-dimensional movement , 2010, Journal of neural engineering.

[20]  B. Blankertz,et al.  Unsupervised adaptation of the LDA classifier for Brain-Computer interfaces , 2008 .

[21]  Horst Bischof,et al.  The Self-Paced Graz Brain-Computer Interface: Methods and Applications , 2007, Comput. Intell. Neurosci..

[22]  Dingguo Zhang,et al.  Improved GMM with parameter initialization for unsupervised adaptation of Brain–Computer interface , 2010 .

[23]  Eilon Vaadia,et al.  Motor Cortex in Voluntary Movements: A Distributed System for Distributed Functions , 2007 .

[24]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[25]  John Q. Gan,et al.  A Hybrid Approach to Feature Subset Selection for Brain-Computer Interface Design , 2011, IDEAL.

[26]  Benjamin Blankertz,et al.  THE BERLIN BRAIN-COMPUTER INTERFACE PRESENTS THE NOVEL MENTAL TYPEWRITER HEX-O-SPELL , 2006 .

[27]  J. Blumberg,et al.  Adaptive Classification for Brain Computer Interfaces , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[28]  Christa Neuper,et al.  Motor imagery and ERD , 1999 .

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

[30]  Motoaki Kawanabe,et al.  Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing , 2007, NIPS.