The wadsworth BCI research and development program: at home with BCI

The ultimate goal of brain-computer interface (BCI) technology is to provide communication and control capacities to people with severe motor disabilities. BCI research at the Wadsworth Center focuses primarily on noninvasive, electroencephalography (EEG)-based BCI methods. We have shown that people, including those with severe motor disabilities, can learn to use sensorimotor rhythms (SMRs) to move a cursor rapidly and accurately in one or two dimensions. We have also improved P300-based BCI operation. We are now translating this laboratory-proven BCI technology into a system that can be used by severely disabled people in their homes with minimal ongoing technical oversight. To accomplish this, we have: improved our general-purpose BCI software (BCI2000); improved online adaptation and feature translation for SMR-based BCI operation; improved the accuracy and bandwidth of P300-based BCI operation; reduced the complexity of system hardware and software and begun to evaluate home system use in appropriate users. These developments have resulted in prototype systems for every day use in people's homes.

[1]  H. Flor,et al.  A spelling device for the paralysed , 1999, Nature.

[2]  D.J. McFarland,et al.  The Wadsworth Center brain-computer interface (BCI) research and development program , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  W. A. Sarnacki,et al.  Brain–computer interface (BCI) operation: optimizing information transfer rates , 2003, Biological Psychology.

[4]  F. Piccione,et al.  P300-based brain computer interface: Reliability and performance in healthy and paralysed participants , 2006, Clinical Neurophysiology.

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

[6]  M.M. Moore,et al.  Real-world applications for brain-computer interface technology , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  John P. Donoghue,et al.  Connecting cortex to machines: recent advances in brain interfaces , 2002, Nature Neuroscience.

[8]  E. Donchin,et al.  A P300-based brain–computer interface: Initial tests by ALS patients , 2006, Clinical Neurophysiology.

[9]  Jessica D. Bayliss,et al.  Changing the P300 Brain Computer Interface , 2004, Cyberpsychology Behav. Soc. Netw..

[10]  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.

[11]  J.R. Wolpaw,et al.  A $\mu $-Rhythm Matched Filter for Continuous Control of a Brain-Computer Interface , 2007, IEEE Transactions on Biomedical Engineering.

[12]  G. Pfurtscheller,et al.  Conversion of EEG activity into cursor movement by a brain-computer interface (BCI) , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[14]  F. D. Silva Neural mechanisms underlying brain waves: from neural membranes to networks. , 1991 .

[15]  J. Wolpaw,et al.  Mu and Beta Rhythm Topographies During Motor Imagery and Actual Movements , 2004, Brain Topography.

[16]  Helge J. Ritter,et al.  Improving Transfer Rates in Brain Computer Interfacing: A Case Study , 2002, NIPS.

[17]  J. Cedarbaum,et al.  The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function , 1999, Journal of the Neurological Sciences.

[18]  Hiroshi Mitsumoto,et al.  Predictors and course of elective long‐term mechanical ventilation: A prospective study of ALS patients , 2006, Amyotrophic lateral sclerosis : official publication of the World Federation of Neurology Research Group on Motor Neuron Diseases.

[19]  D.J. McFarland,et al.  Sensorimotor rhythm-based brain-computer interface (BCI): feature selection by regression improves performance , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[21]  Glyn Walsh,et al.  A simple new method for the construction of a ptosis crutch , 2006, Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians.

[22]  B. Z. Allison,et al.  Independent component analysis applications in a P300 brain computer interface , 2003 .

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

[24]  M B Bromberg,et al.  Assessing health status quality of life in ALS: Comparison of the SIP/ALS-19 with the ALS Functional Rating Scale and the Short Form-12 Health Survey , 2001, Amyotrophic lateral sclerosis and other motor neuron disorders : official publication of the World Federation of Neurology, Research Group on Motor Neuron Diseases.

[25]  J. Wolpaw,et al.  Answering questions with an electroencephalogram-based brain-computer interface. , 1998, Archives of physical medicine and rehabilitation.

[26]  Jonathan R Wolpaw,et al.  EEG-Based Communication and Control: Speed–Accuracy Relationships , 2003, Applied psychophysiology and biofeedback.

[27]  Dennis J. McFarland,et al.  Electroencephalographic(EEG)-based communication: EEG control versus system performance in humans , 2003, Neuroscience Letters.

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

[29]  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.

[30]  J. Wolpaw,et al.  A P300-based brain–computer interface for people with amyotrophic lateral sclerosis , 2008, Clinical Neurophysiology.

[31]  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.

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

[33]  J. Wolpaw,et al.  A P300-based brain–computer interface for people with amyotrophic lateral sclerosis , 2008, Clinical Neurophysiology.

[34]  G. Pfurtscheller,et al.  Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. , 2005, Brain research. Cognitive brain research.

[35]  Dennis J. McFarland,et al.  Brain-computer interface (BCI) operation: signal and noise during early training sessions , 2005, Clinical Neurophysiology.

[36]  M. Doyle,et al.  Trends in augmentative and alternative communication use by individuals with amyotrophic lateral sclerosis , 2001 .

[37]  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.

[38]  J. Wolpaw,et al.  Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface , 2005, Neurology.

[39]  J. Wolpaw,et al.  Brain-computer communication: unlocking the locked in. , 2001, Psychological bulletin.

[40]  H. Lüders,et al.  American Electroencephalographic Society Guidelines for Standard Electrode Position Nomenclature , 1991, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[41]  Gerwin Schalk,et al.  A brain–computer interface using electrocorticographic signals in humans , 2004, Journal of neural engineering.

[42]  F. Portier,et al.  Evaluation of home versus laboratory polysomnography in the diagnosis of sleep apnea syndrome. , 2000, American journal of respiratory and critical care medicine.

[43]  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.

[44]  Zhongming Liu,et al.  An enhanced time-frequency-spatial approach for motor imagery classification , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[45]  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.

[46]  G. Pfurtscheller,et al.  EEG-based communication: presence of an error potential , 2000, Clinical Neurophysiology.

[47]  J. Wolpaw,et al.  Multichannel EEG-based brain-computer communication. , 1994, Electroencephalography and clinical neurophysiology.