%0 Journal Article %J Neuroimage %D 2007 %T An MEG-based brain-computer interface (BCI). %A Mellinger, Jürgen %A Gerwin Schalk %A Christoph Braun %A Preissl, Hubert %A Rosenstiel, W. %A Niels Birbaumer %A Kübler, A. %K Adult %K Algorithms %K Artifacts %K Brain %K Electroencephalography %K Electromagnetic Fields %K Electromyography %K Feedback %K Female %K Foot %K Hand %K Head Movements %K Humans %K Magnetic Resonance Imaging %K Magnetoencephalography %K Male %K Movement %K Principal Component Analysis %K Signal Processing, Computer-Assisted %K User-Computer Interface %X

Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography(EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor mu and beta rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant mu rhythm self control within 32 min of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.

%B Neuroimage %V 36 %P 581-93 %8 07/2007 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/17475511 %N 3 %R 10.1016/j.neuroimage.2007.03.019 %0 Journal Article %J IEEE Trans Neural Syst Rehabil Eng %D 2006 %T Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects. %A Jeremy Jeremy Hill %A Lal, T.N %A Schröder, Michael %A Hinterberger, T. %A Wilhelm, Barbara %A Nijboer, F %A Mochty, Ursula %A Widman, Guido %A Elger, Christian %A Schölkopf, B %A Kübler, A. %A Niels Birbaumer %K Algorithms %K Artificial Intelligence %K Cluster Analysis %K Computer User Training %K Electroencephalography %K Evoked Potentials %K Female %K Humans %K Imagination %K Male %K Middle Aged %K Paralysis %K Pattern Recognition, Automated %K User-Computer Interface %X

We summarize results from a series of related studies that aim to develop a motor-imagery-based brain-computer interface using a single recording session of electroencephalogram (EEG) or electrocorticogram (ECoG) signals for each subject. We apply the same experimental and analytical methods to 11 nonparalysed subjects (eight EEG, three ECoG), and to five paralyzed subjects (four EEG, one ECoG) who had been unable to communicate for some time. While it was relatively easy to obtain classifiable signals quickly from most of the nonparalyzed subjects, it proved impossible to classify the signals obtained from the paralyzed patients by the same methods. This highlights the fact that though certain BCI paradigms may work well with healthy subjects, this does not necessarily indicate success with the target user group. We outline possible reasons for this failure to transfer.

%B IEEE Trans Neural Syst Rehabil Eng %V 14 %P 183-6 %8 06/2006 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/16792289 %N 2 %R 10.1109/TNSRE.2006.875548 %0 Journal Article %J Neurology %D 2005 %T Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. %A Kübler, A. %A Nijboer, F %A Mellinger, Jürgen %A Theresa M Vaughan %A Pawelzik, H %A Gerwin Schalk %A Dennis J. McFarland %A Niels Birbaumer %A Jonathan Wolpaw %K Aged %K Amyotrophic Lateral Sclerosis %K Electroencephalography %K Evoked Potentials, Motor %K Evoked Potentials, Somatosensory %K Female %K Humans %K Imagination %K Male %K Middle Aged %K Motor Cortex %K Movement %K Paralysis %K Photic Stimulation %K Prostheses and Implants %K Somatosensory Cortex %K Treatment Outcome %K User-Computer Interface %X

People with severe motor disabilities can maintain an acceptable quality of life if they can communicate. Brain-computer interfaces (BCIs), which do not depend on muscle control, can provide communication. Four people severely disabled by ALS learned to operate a BCI with EEG rhythms recorded over sensorimotor cortex. These results suggest that a sensorimotor rhythm-based BCI could help maintain quality of life for people with ALS.

%B Neurology %V 64 %P 1775-7 %8 05/2005 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/15911809 %N 10 %R 10.1212/01.WNL.0000158616.43002.6D %0 Journal Article %J Neurology %D 2005 %T Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. %A Kübler, A. %A Nijboer, F. %A Mellinger, J. %A Theresa M Vaughan %A Pawelzik, H. %A Gerwin Schalk %A Dennis J. McFarland %A Niels Birbaumer %A Jonathan Wolpaw %K User-Computer Interface %X People with severe motor disabilities can maintain an acceptable quality of life if they can communicate. Brain-computer interfaces (BCIs), which do not depend on muscle control, can provide communication. Four people severely disabled by ALS learned to operate a BCI with EEG rhythms recorded over sensorimotor cortex. These results suggest that a sensorimotor rhythm-based BCI could help maintain quality of life for people with ALS. %B Neurology %V 64 %P 1775–1777 %8 05/2005 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/15911809 %R 10.1212/01.WNL.0000158616.43002.6D %0 Journal Article %J Psychological bulletin %D 2001 %T Brain-computer communication: unlocking the locked in. %A Kübler, A. %A Kotchoubey, B. %A Kaiser, J. %A Jonathan Wolpaw %A Niels Birbaumer %K User-Computer Interface %X With the increasing efficiency of life-support systems and better intensive care, more patients survive severe injuries of the brain and spinal cord. Many of these patients experience locked-in syndrome: The active mind is locked in a paralyzed body. Consequently, communication is extremely restricted or impossible. A muscle-independent communication channel overcomes this problem and is realized through a brain-computer interface, a direct connection between brain and computer. The number of technically elaborated brain-computer interfaces is in contrast with the number of systems used in the daily life of locked-in patients. It is hypothesized that a profound knowledge and consideration of psychological principles are necessary to make brain-computer interfaces feasible for locked-in patients. %B Psychological bulletin %V 127 %P 358–375 %8 05/2001 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/11393301 %R 10.1037/0033-2909.127.3.358