@article {2131, title = {Transition from the locked in to the completely locked-in state: a physiological analysis.}, journal = {Clin Neurophysiol}, volume = {122}, year = {2011}, month = {06/2011}, pages = {925-33}, abstract = {

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.

}, keywords = {Adult, Amyotrophic Lateral Sclerosis, Area Under Curve, Brain, Communication Aids for Disabled, Disease Progression, Electroencephalography, Electromyography, Humans, Male, Signal Processing, Computer-Assisted, User-Computer Interface}, issn = {1872-8952}, doi = {10.1016/j.clinph.2010.08.019}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20888292}, author = {Murguialday, A Ramos and Jeremy Jeremy Hill and Bensch, M and Martens, S M M and S Halder and Nijboer, F and Schoelkopf, Bernhard and Niels Birbaumer and Gharabaghi, A} } @article {3199, title = {A P300-based brain-computer interface for people with amyotrophic lateral sclerosis.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {119}, year = {2008}, month = {08/2008}, pages = {1909{\textendash}1916}, abstract = {OBJECTIVE: The current study evaluates the efficacy of a P300-based brain-computer interface (BCI) communication device for individuals with advanced ALS. METHODS: Participants attended to one cell of a N x N matrix while the N rows and N columns flashed randomly. Each cell of the matrix contained one character. Every flash of an attended character served as a rare event in an oddball sequence and elicited a P300 response. Classification coefficients derived using a stepwise linear discriminant function were applied to the data after each set of flashes. The character receiving the highest discriminant score was presented as feedback. RESULTS: In Phase I, six participants used a 6 x 6 matrix on 12 separate days with a mean rate of 1.2 selections/min and mean online and offline accuracies of 62\% and 82\%, respectively. In Phase II, four participants used either a 6 x 6 or a 7 x 7 matrix to produce novel and spontaneous statements with a mean online rate of 2.1 selections/min and online accuracy of 79\%. The amplitude and latency of the P300 remained stable over 40 weeks. CONCLUSIONS: Participants could communicate with the P300-based BCI and performance was stable over many months. SIGNIFICANCE: BCIs could provide an alternative communication and control technology in the daily lives of people severely disabled by ALS.}, keywords = {Amyotrophic Lateral Sclerosis, brain-computer interface, electroencephalogram, event-related potentials, P300, Rehabilitation}, issn = {1388-2457}, doi = {10.1016/j.clinph.2008.03.034}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18571984}, author = {Nijboer, F. and Sellers, E. W. and Mellinger, J. and Jordan, M. A. and Matuz, T. and Adrian Furdea and S Halder and Mochty, U. and Krusienski, D. J. and Theresa M Vaughan and Jonathan Wolpaw and Niels Birbaumer and K{\"u}bler, A.} } @article {2169, title = {Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface.}, journal = {Neurology}, volume = {64}, year = {2005}, month = {05/2005}, pages = {1775-7}, abstract = {

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.

}, keywords = {Aged, Amyotrophic Lateral Sclerosis, Electroencephalography, Evoked Potentials, Motor, Evoked Potentials, Somatosensory, Female, Humans, Imagination, Male, Middle Aged, Motor Cortex, Movement, Paralysis, Photic Stimulation, Prostheses and Implants, Somatosensory Cortex, Treatment Outcome, User-Computer Interface}, issn = {1526-632X}, doi = {10.1212/01.WNL.0000158616.43002.6D}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15911809}, author = {K{\"u}bler, A. and Nijboer, F and Mellinger, J{\"u}rgen and Theresa M Vaughan and Pawelzik, H and Gerwin Schalk and Dennis J. McFarland and Niels Birbaumer and Jonathan Wolpaw} } @article {2167, title = {The BCI Competition 2003: Progress and perspectives in detection and discrimination of EEG single trials.}, journal = {IEEE Trans Biomed Eng}, volume = {51}, year = {2004}, month = {06/2004}, pages = {1044-51}, abstract = {Interest in developing a new method of man-to-machine communication--a brain-computer interface (BCI)--has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.}, keywords = {Adult, Algorithms, Amyotrophic Lateral Sclerosis, Artificial Intelligence, Brain, Cognition, Databases, Factual, Electroencephalography, Evoked Potentials, Humans, Reproducibility of Results, Sensitivity and Specificity, User-Computer Interface}, issn = {0018-9294}, doi = {10.1109/TBME.2004.826692}, author = {Benjamin Blankertz and M{\"u}ller, Klaus-Robert and Curio, Gabriel and Theresa M Vaughan and Gerwin Schalk and Jonathan Wolpaw and Schl{\"o}gl, Alois and Neuper, Christa and Pfurtscheller, Gert and Hinterberger, T. and Schr{\"o}der, Michael and Niels Birbaumer} }