Brain-Computer Interfacing: How to control computers with thoughts

Brain-Computer Interface (BCI) technology aims at providing communication and control facilities to severely paralyzed people. These patients are not able to manipulate objects or communicate their needs, even though their mental capabilities are intact. Electroencephalographic (EEG) signals recorded from the scalp can be used to decode wishes and intentions. BCI approaches are based on a variety of strategies to generate control signals. For example, the control signals may be the result of visual or auditive stimulation or of imaginary motor tasks. The control signals are analyzed by a translation algorithm which associates a signal to a command. Thus, BCI provides a communication channel not based on nerves and muscles. This paper describes the BCI systems developed at the Center for Sensory-Motor Interaction of Aalborg University, with special emphasis on strategies based on non-motor imagery tasks.

[1]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[2]  C. Burrus,et al.  Introduction to Wavelets and Wavelet Transforms: A Primer , 1997 .

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

[4]  Michael Voigt,et al.  Movement-related parameters modulate cortical activity during imaginary isometric plantar-flexions , 2006, Experimental Brain Research.

[5]  K.D. Nielsen,et al.  EEG based BCI-towards a better control. Brain-computer interface research at aalborg university , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  M. Stokes,et al.  Cognitive tasks for driving a brain-computer interfacing system: a pilot study , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  Kim Dremstrup,et al.  Steady-State Visual Evoked Potentials to drive a Brain Computer Interface , 2008 .

[8]  Kim Dremstrup,et al.  Auditory and spatial navigation imagery in Brain–Computer Interface using optimized wavelets , 2008, Journal of Neuroscience Methods.

[9]  Marie-Françoise Lucas,et al.  Optimization of wavelets for classification of movement-related cortical potentials generated by variation of force-related parameters , 2007, Journal of Neuroscience Methods.

[10]  B. Rockstroh,et al.  Biofeedback of slow cortical potentials. I. , 1980, Electroencephalography and clinical neurophysiology.

[11]  Dario Farina,et al.  Movement-Related Cortical Potentials Allow Discrimination of Rate of Torque Development in Imaginary Isometric Plantar Flexion , 2008, IEEE Transactions on Biomedical Engineering.

[12]  Ramesh A. Gopinath,et al.  Wavelets and Wavelet Transforms , 1998 .

[13]  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.

[14]  G Calhoun,et al.  Brain-computer interfaces based on the steady-state visual-evoked response. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[15]  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.

[16]  Michael Voigt,et al.  Relationship between plantar-flexor torque generation and the magnitude of the movement-related potentials , 2004, Experimental Brain Research.