TY - JOUR T1 - Transition from the locked in to the completely locked-in state: a physiological analysis. JF - Clin Neurophysiol Y1 - 2011 A1 - Murguialday, A Ramos A1 - Jeremy Jeremy Hill A1 - Bensch, M A1 - Martens, S M M A1 - S Halder A1 - Nijboer, F A1 - Schoelkopf, Bernhard A1 - Niels Birbaumer A1 - Gharabaghi, A KW - Adult KW - Amyotrophic Lateral Sclerosis KW - Area Under Curve KW - Brain KW - Communication Aids for Disabled KW - Disease Progression KW - Electroencephalography KW - Electromyography KW - Humans KW - Male KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB -

OBJECTIVE: 

To clarify the physiological and behavioral boundaries between locked-in (LIS) and the completely locked-in state (CLIS) (no voluntary eye movements, no communication possible) through electrophysiological data and to secure brain-computer-interface (BCI) communication.

METHODS: 

Electromyography from facial muscles, external anal sphincter (EAS), electrooculography and electrocorticographic data during different psychophysiological tests were acquired to define electrophysiological differences in an amyotrophic lateral sclerosis (ALS) patient with an intracranially implanted grid of 112 electrodes for nine months while the patient passed from the LIS to the CLIS.

RESULTS: 

At the very end of the LIS there was no facial muscle activity, nor external anal sphincter but eye control. Eye movements were slow and lasted for short periods only. During CLIS event related brainpotentials (ERP) to passive limb movements and auditory stimuli were recorded, vibrotactile stimulation of different body parts resulted in no ERP response.

CONCLUSIONS: 

The results presented contradict the commonly accepted assumption that the EAS is the last remaining muscle under voluntary control and demonstrate complete loss of eye movements in CLIS. The eye muscle was shown to be the last muscle group under voluntary control. The findings suggest ALS as a multisystem disorder, even affecting afferent sensory pathways.

SIGNIFICANCE: 

Auditory and proprioceptive brain-computer-interface (BCI) systems are the only remaining communication channels in CLIS.

VL - 122 UR - http://www.ncbi.nlm.nih.gov/pubmed/20888292 IS - 5 ER - TY - JOUR T1 - Voluntary brain regulation and communication with electrocorticogram signals. JF - Epilepsy Behav Y1 - 2008 A1 - Hinterberger, T. A1 - Widman, Guido A1 - Lal, T.N A1 - Jeremy Jeremy Hill A1 - Tangermann, Michael A1 - Rosenstiel, W. A1 - Schölkopf, B A1 - Elger, Christian A1 - Niels Birbaumer KW - Adult KW - Biofeedback, Psychology KW - Cerebral Cortex KW - Communication Aids for Disabled KW - Dominance, Cerebral KW - Electroencephalography KW - Epilepsies, Partial KW - Female KW - Humans KW - Imagination KW - Male KW - Middle Aged KW - Motor Activity KW - Motor Cortex KW - Signal Processing, Computer-Assisted KW - Software KW - Somatosensory Cortex KW - Theta Rhythm KW - User-Computer Interface KW - Writing AB -

Brain-computer interfaces (BCIs) can be used for communication in writing without muscular activity or for learning to control seizures by voluntary regulation of brain signals such as the electroencephalogram (EEG). Three of five patients with epilepsy were able to spell their names with electrocorticogram (ECoG) signals derived from motor-related areas within only one or two training sessions. Imagery of finger or tongue movements was classified with support-vector classification of autoregressive coefficients derived from the ECoG signals. After training of the classifier, binary classification responses were used to select letters from a computer-generated menu. Offline analysis showed increased theta activity in the unsuccessful patients, whereas the successful patients exhibited dominant sensorimotor rhythms that they could control. The high spatial resolution and increased signal-to-noise ratio in ECoG signals, combined with short training periods, may offer an alternative for communication in complete paralysis, locked-in syndrome, and motor restoration.

VL - 13 UR - http://www.ncbi.nlm.nih.gov/pubmed/18495541 IS - 2 ER - TY - JOUR T1 - An MEG-based brain-computer interface (BCI). JF - Neuroimage Y1 - 2007 A1 - Mellinger, Jürgen A1 - Gerwin Schalk A1 - Christoph Braun A1 - Preissl, Hubert A1 - Rosenstiel, W. A1 - Niels Birbaumer A1 - Kübler, A. KW - Adult KW - Algorithms KW - Artifacts KW - Brain KW - Electroencephalography KW - Electromagnetic Fields KW - Electromyography KW - Feedback KW - Female KW - Foot KW - Hand KW - Head Movements KW - Humans KW - Magnetic Resonance Imaging KW - Magnetoencephalography KW - Male KW - Movement KW - Principal Component Analysis KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB -

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.

VL - 36 UR - http://www.ncbi.nlm.nih.gov/pubmed/17475511 IS - 3 ER - TY - JOUR T1 - Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2006 A1 - Jeremy Jeremy Hill A1 - Lal, T.N A1 - Schröder, Michael A1 - Hinterberger, T. A1 - Wilhelm, Barbara A1 - Nijboer, F A1 - Mochty, Ursula A1 - Widman, Guido A1 - Elger, Christian A1 - Schölkopf, B A1 - Kübler, A. A1 - Niels Birbaumer KW - Algorithms KW - Artificial Intelligence KW - Cluster Analysis KW - Computer User Training KW - Electroencephalography KW - Evoked Potentials KW - Female KW - Humans KW - Imagination KW - Male KW - Middle Aged KW - Paralysis KW - Pattern Recognition, Automated KW - User-Computer Interface AB -

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.

VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792289 IS - 2 ER - TY - JOUR T1 - Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. JF - Neurology Y1 - 2005 A1 - Kübler, A. A1 - Nijboer, F A1 - Mellinger, Jürgen A1 - Theresa M Vaughan A1 - Pawelzik, H A1 - Gerwin Schalk A1 - Dennis J. McFarland A1 - Niels Birbaumer A1 - Jonathan Wolpaw KW - Aged KW - Amyotrophic Lateral Sclerosis KW - Electroencephalography KW - Evoked Potentials, Motor KW - Evoked Potentials, Somatosensory KW - Female KW - Humans KW - Imagination KW - Male KW - Middle Aged KW - Motor Cortex KW - Movement KW - Paralysis KW - Photic Stimulation KW - Prostheses and Implants KW - Somatosensory Cortex KW - Treatment Outcome KW - User-Computer Interface AB -

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.

VL - 64 UR - http://www.ncbi.nlm.nih.gov/pubmed/15911809 IS - 10 ER -