Brain-computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms.

The Wadsworth brain-computer interface (BCI), based on mu and beta sensorimotor rhythms, uses one- and two-dimensional cursor movement tasks and relies on user training. This is a real-time closed-loop system. Signal processing consists of channel selection, spatial filtering, and spectral analysis. Feature translation uses a regression approach and normalization. Adaptation occurs at several points in this process on the basis of different criteria and methods. It can use either feedforward (e.g., estimating the signal mean for normalization) or feedback control (e.g., estimating feature weights for the prediction equation). We view this process as the interaction between a dynamic user and a dynamic system that coadapt over time. Understanding the dynamics of this interaction and optimizing its performance represent a major challenge for BCI research.

[1]  Dennis J. McFarland,et al.  Design and operation of an EEG-based brain-computer interface with digital signal processing technology , 1997 .

[2]  D J McFarland,et al.  Brain-computer interface research at the Wadsworth Center. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[3]  Dawn M. Taylor,et al.  Direct Cortical Control of 3D Neuroprosthetic Devices , 2002, Science.

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

[5]  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.

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

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

[8]  N. Birbaumer,et al.  The thought translation device: a neurophysiological approach to communication in total motor paralysis , 1999, Experimental Brain Research.

[9]  Niels Birbaumer,et al.  The thought-translation device , 2007 .

[10]  J. Kalaska,et al.  Motor cortex neural correlates of output kinematics and kinetics during isometric-force and arm-reaching tasks. , 2005, Journal of neurophysiology.

[11]  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.

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

[13]  Gert Pfurtscheller,et al.  Characterization of four-class motor imagery EEG data for the BCI-competition 2005 , 2005, Journal of neural engineering.

[14]  Klaus-Robert Müller,et al.  The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials , 2004, IEEE Transactions on Biomedical Engineering.

[15]  Erich E. Sutter,et al.  The brain response interface: communication through visually-induced electrical brain responses , 1992 .

[16]  A Kostov,et al.  Parallel man-machine training in development of EEG-based cursor control. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

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

[18]  G. Pfurtscheller,et al.  EEG-Based Communication: Evaluation of Alternative Signal Prediction Methods - EEG-basierte Kommunikation: Evaluierung alternativer Methoden zur Signalprädiktion , 1997, Biomedizinische Technik. Biomedical engineering.

[19]  B.Z. Allison,et al.  ERPs evoked by different matrix sizes: implications for a brain computer interface (BCI) system , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[21]  J R Wolpaw,et al.  Spatial filter selection for EEG-based communication. , 1997, Electroencephalography and clinical neurophysiology.

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

[23]  K.-R. Muller,et al.  Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[24]  Gert Pfurtscheller,et al.  Brain-computer interface: a new communication device for handicapped persons , 1993 .

[25]  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.

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

[27]  Miguel A. L. Nicolelis,et al.  Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex , 1999, Nature Neuroscience.

[28]  Dennis J. McFarland,et al.  An EEG-based method for graded cursor control , 1993, Psychobiology.