Towards Development of a 3-State Self-Paced Brain-Computer Interface

Most existing brain-computer interfaces (BCIs) detect specific mental activity in a so-called synchronous paradigm. Unlike synchronous systems which are operational at specific system-defined periods, self-paced (asynchronous) interfaces have the advantage of being operational at all times. The low-frequency asynchronous switch design (LF-ASD) is a 2-state self-paced BCI that detects the presence of a specific finger movement in the ongoing EEG. Recent evaluations of the 2-state LF-ASD show an average true positive rate of 41% at the fixed false positive rate of 1%. This paper proposes two designs for a 3-state self-paced BCI that is capable of handling idle brain state. The two proposed designs aim at detecting right- and left-hand extensions from the ongoing EEG. They are formed of two consecutive detectors. The first detects the presence of a right- or a left-hand movement and the second classifies the detected movement as a right or a left one. In an offline analysis of the EEG data collected from four able-bodied individuals, the 3-state brain-computer interface shows a comparable performance with a 2-state system and significant performance improvement if used as a 2-state BCI, that is, in detecting the presence of a right- or a left-hand movement (regardless of the type of movement). It has an average true positive rate of 37.5% and 42.8% (at false positives rate of 1%) in detecting right- and left-hand extensions, respectively, in the context of a 3-state self-paced BCI and average detection rate of 58.1% (at false positive rate of 1%) in the context of a 2-state self-paced BCI.

[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]  William Z Rymer,et al.  Guest Editorial Brain–Computer Interface Technology: A Review of the Second International Meeting , 2001 .

[3]  Bernhard Graimann,et al.  Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis , 2004, IEEE Transactions on Biomedical Engineering.

[4]  G. Pfurtscheller,et al.  Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement. , 1979, Electroencephalography and clinical neurophysiology.

[5]  Christa Neuper,et al.  An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate , 2004, IEEE Transactions on Biomedical Engineering.

[6]  R. Ward,et al.  A Comparative Study on Generating Training-Data for Self-Paced Brain Interfaces , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[8]  Gilles Blanchard,et al.  BCI competition 2003-data set IIa: spatial patterns of self-controlled brain rhythm modulations , 2004, IEEE Transactions on Biomedical Engineering.

[9]  Chng Eng Siong,et al.  High accuracy classification of EEG signal , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[10]  Thomas L. Ainsworth,et al.  Unsupervised classification of polarimetric synthetic aperture Radar images using fuzzy clustering and EM clustering , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[12]  Klaus-Robert Müller,et al.  Combining Features for BCI , 2002, NIPS.

[13]  K.-R. Muller,et al.  The Berlin brain-computer interface: EEG-based communication without subject training , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  M. Glavinovic,et al.  Comparison of Parzen density and frequency histogram as estimators of probability density functions , 1996, Pflügers Archiv.

[15]  Jaimie F Borisoff,et al.  Real-Time Control of a Video Game With a Direct Brain–Computer Interface , 2004, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[16]  Reinhold Scherer,et al.  Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components , 2005, Journal of neural engineering.

[17]  Stephen Grossberg,et al.  Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system , 1991, Neural Networks.

[18]  Jong-Sen Lee,et al.  The use of fully polarimetric information for the fuzzy neural classification of SAR images , 2003, IEEE Trans. Geosci. Remote. Sens..

[19]  G.E. Birch,et al.  A general framework for brain-computer interface design , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[20]  Gary E. Birch,et al.  A hybrid genetic algorithm approach for improving the performance of the LF-ASD brain computer interface , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[21]  J. Mourino,et al.  Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[22]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[23]  Klaus-Robert Müller,et al.  Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms , 2004, IEEE Transactions on Biomedical Engineering.

[24]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces to restore motor function and probe neural circuits , 2003, Nature Reviews Neuroscience.

[25]  G. Birch,et al.  Single-trial processing of event-related potentials using outlier information , 1993, IEEE Transactions on Biomedical Engineering.

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

[27]  Febo Cincotti,et al.  Human Movement-Related Potentials vs Desynchronization of EEG Alpha Rhythm: A High-Resolution EEG Study , 1999, NeuroImage.

[28]  J. Wolpaw.  Chapter 64 Brain-computer interfaces (BCIs) for communication and control: a mini-review , 2004 .

[29]  G. F. Inbar,et al.  Detection of movement-related potentials from the electro-encephalogram for possible use in a brain-computer interface , 2006, Medical and Biological Engineering and Computing.

[30]  Rabab K Ward,et al.  An improved asynchronous brain interface: making use of the temporal history of the LF-ASD feature vectors. , 2006, Journal of neural engineering.

[31]  Karla Felix Navarro,et al.  A Comprehensive Survey of Brain Interface Technology Designs , 2007, Annals of Biomedical Engineering.

[32]  Gary E. Birch,et al.  A brain-controlled switch for asynchronous control applications , 2000, IEEE Trans. Biomed. Eng..

[33]  T. Cacoullos.  Estimation of a multivariate density , 1966 .

[34]  S. Slobounov,et al.  Movement-related EEG potentials are force or end-effector dependent: evidence from a multi-finger experiment , 2002, Clinical Neurophysiology.

[35]  G. Pfurtscheller,et al.  Event-related desynchronisation of central beta-rhythms during brisk and slow self-paced finger movements of dominant and nondominant hand. , 1996, Brain research. Cognitive brain research.

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

[37]  G Pfurtscheller,et al.  Frequency component selection for an EEG-based brain to computer interface. , 1999, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

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

[39]  Fusheng Yang,et al.  BCI competition 2003-data set IV:An algorithm based on CSSD and FDA for classifying single-trial EEG , 2004, IEEE Transactions on Biomedical Engineering.

[40]  Jing Z. Liu,et al.  Brain activation during human finger extension and flexion movements , 2000, Brain Research.

[41]  Gary E. Birch,et al.  Brain-computer interface design for asynchronous control applications: improvements to the LF-ASD asynchronous brain switch , 2004, IEEE Transactions on Biomedical Engineering.

[42]  Rabab K Ward,et al.  A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals , 2007, Journal of neural engineering.

[43]  Christa Neuper,et al.  Graz Brain-Computer Interface (BCI) II , 1994, ICCHP.

[44]  Gary E. Birch,et al.  Enhancing the performance of the LF-ASD brain-computer interface , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[45]  Jukka Heikkonen,et al.  A local neural classifier for the recognition of EEG patterns associated to mental tasks , 2002, IEEE Trans. Neural Networks.

[46]  G. Pfurtscheller,et al.  Foot and hand area mu rhythms. , 1997, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[47]  G. Birch,et al.  Initial on-line evaluations of the LF-ASD brain-computer interface with able-bodied and spinal-cord subjects using imagined voluntary motor potentials , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[48]  Gary E. Birch,et al.  Designing pointing devices using brain-computer interface technology , 2003, First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings..

[49]  A. Bashashati,et al.  An automatic method to generate ensemble averages of movement-related potentials for individuals with spinal cord injuries , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[50]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[51]  Horst Bischof,et al.  Toward Self-Paced Brain–Computer Communication: Navigation Through Virtual Worlds , 2008, IEEE Transactions on Biomedical Engineering.

[52]  S P Levine,et al.  A direct brain interface based on event-related potentials. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[53]  Rabab K. Ward,et al.  User Customization of the Feature Generator of an Asynchronous Brain Interface , 2006, Annals of Biomedical Engineering.

[54]  Clemens Brunner,et al.  Online Control of a Brain-Computer Interface Using Phase Synchronization , 2006, IEEE Transactions on Biomedical Engineering.

[55]  P. Lachenbruch,et al.  Paired t Test , 2008 .