04388nas a2200385 4500008004100000022001400041245009500055210006900150260001200219300001100231490000800242520325500250653001003505653003403515653002103549653001003570653003603580653002403616653002703640653002103667653001103688653000903699653004103708653002803749100002603777700002503803700001403828700001903842700001403861700001503875700002503890700002103915700001803936856004803954 2011 eng d a1872-895200aTransition from the locked in to the completely locked-in state: a physiological analysis.0 aTransition from the locked in to the completely lockedin state a c06/2011 a925-330 v1223 a

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.

10aAdult10aAmyotrophic Lateral Sclerosis10aArea Under Curve10aBrain10aCommunication Aids for Disabled10aDisease Progression10aElectroencephalography10aElectromyography10aHumans10aMale10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aMurguialday, Ramos, A1 aHill, Jeremy, Jeremy1 aBensch, M1 aMartens, S M M1 aHalder, S1 aNijboer, F1 aSchoelkopf, Bernhard1 aBirbaumer, Niels1 aGharabaghi, A uhttp://www.ncbi.nlm.nih.gov/pubmed/2088829201846nas a2200241 4500008004100000245008500041210006900126260001200195520097600207653003001183653005001213100001301263700002301276700002501299700002001324700002001344700002301364700001901387700001801406700002101424700002501445856013401470 2005 eng d00aA Brain Computer Interface with Online Feedback based on Magnetoencephalography.0 aBrain Computer Interface with Online Feedback based on Magnetoen c08/20053 a

The aim of this paper is to show that machine learning techniques can be used to derive a classifying function for human brain signal data measured by magnetoencephalography (MEG), for the use in a brain computer interface (BCI). This is especially helpful for evaluating quickly whether a BCI approach based on electroencephalography, on which training may be slower due to lower signalto-noise ratio, is likely to succeed. We apply RCE and regularized SVMs to the experimental data of ten healthy subjects performing a motor imagery task. Four subjects were able to use a trained classifier to write a short name. Further analysis gives evidence that the proposed imagination task is suboptimal for the possible extension to a multiclass interface. To the best of our knowledge this paper is the first working online MEG-based BCI and is therefore a “proof of concept”.

10aBrain Computer Interfaces10aUser Modelling for Computer Human Interaction1 aLal, T N1 aSchroeder, Michael1 aHill, Jeremy, Jeremy1 aPreissl, Hubert1 aHinterberger, T1 aMellinger, Jürgen1 aBogdan, Martin1 aRosenstiel, W1 aBirbaumer, Niels1 aSchoelkopf, Bernhard uhttp://www.researchgate.net/publication/221346004_A_brain_computer_interface_with_online_feedback_based_on_magnetoencephalography