%0 Journal Article %J Amyotroph Lateral Scler Frontotemporal Degener %D 2014 %T Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis. %A McCane, Lynn M %A Sellers, Eric W %A Dennis J. McFarland %A Mak, Joseph N %A Carmack, C Steve %A Zeitlin, Debra %A Jonathan Wolpaw %A Theresa M Vaughan %K Adult %K Aged %K Amyotrophic Lateral Sclerosis %K Biofeedback, Psychology %K brain-computer interfaces %K Communication Disorders %K Electroencephalography %K Event-Related Potentials, P300 %K Female %K Humans %K Male %K Middle Aged %K Online Systems %K Photic Stimulation %K Psychomotor Performance %K Reaction Time %X Brain-computer interfaces (BCIs) might restore communication to people severely disabled by amyotrophic lateral sclerosis (ALS) or other disorders. We sought to: 1) define a protocol for determining whether a person with ALS can use a visual P300-based BCI; 2) determine what proportion of this population can use the BCI; and 3) identify factors affecting BCI performance. Twenty-five individuals with ALS completed an evaluation protocol using a standard 6 × 6 matrix and parameters selected by stepwise linear discrimination. With an 8-channel EEG montage, the subjects fell into two groups in BCI accuracy (chance accuracy 3%). Seventeen averaged 92 (± 3)% (range 71-100%), which is adequate for communication (G70 group). Eight averaged 12 (± 6)% (range 0-36%), inadequate for communication (L40 subject group). Performance did not correlate with disability: 11/17 (65%) of G70 subjects were severely disabled (i.e. ALSFRS-R < 5). All L40 subjects had visual impairments (e.g. nystagmus, diplopia, ptosis). P300 was larger and more anterior in G70 subjects. A 16-channel montage did not significantly improve accuracy. In conclusion, most people severely disabled by ALS could use a visual P300-based BCI for communication. In those who could not, visual impairment was the principal obstacle. For these individuals, auditory P300-based BCIs might be effective. %B Amyotroph Lateral Scler Frontotemporal Degener %V 15 %P 207-15 %8 06/2014 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/24555843 %N 3-4 %R 10.3109/21678421.2013.865750 %0 Journal Article %J J Neural Eng %D 2011 %T Current Trends in Hardware and Software for Brain-Computer Interfaces (BCIs). %A Peter Brunner %A Bianchi, L %A Guger, C %A Cincotti, F %A Gerwin Schalk %K Biofeedback, Psychology %K Brain %K Brain Mapping %K Electroencephalography %K Equipment Design %K Equipment Failure Analysis %K Humans %K Man-Machine Systems %K Software %K User-Computer Interface %X

brain-computer interface (BCI) provides a non-muscular communication channel to people with and without disabilities. BCI devices consist of hardware and software. BCI hardware records signals from the brain, either invasively or non-invasively, using a series of device components. BCI software then translates these signals into device output commands and provides feedback. One may categorize different types of BCI applications into the following four categories: basic research, clinical/translational research, consumer products, and emerging applications. These four categories use BCI hardware and software, but have different sets of requirements. For example, while basic research needs to explore a wide range of system configurations, and thus requires a wide range of hardware and software capabilities, applications in the other three categories may be designed for relatively narrow purposes and thus may only need a very limited subset of capabilities. This paper summarizes technical aspects for each of these four categories of BCI applications. The results indicate that BCI technology is in transition from isolated demonstrations to systematic research and commercial development. This process requires several multidisciplinary efforts, including the development of better integrated and more robust BCI hardware and software, the definition of standardized interfaces, and the developmentof certification, dissemination and reimbursement procedures.

%B J Neural Eng %V 8 %P 025001 %8 04/2011 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/21436536 %N 2 %R 10.1088/1741-2560/8/2/025001 %0 Journal Article %J Proc Natl Acad Sci U S A %D 2010 %T Cortical activity during motor execution, motor imagery, and imagery-based online feedback. %A Miller, K.J. %A Gerwin Schalk %A Fetz, Eberhard E %A den Nijs, Marcel %A Ojemann, J G %A Rao, Rajesh P N %K Adolescent %K Adult %K Biofeedback, Psychology %K Cerebral Cortex %K Child %K Electric Stimulation %K Electrocardiography %K Female %K Humans %K Male %K Middle Aged %K Motor Activity %K Young Adult %X

Imagery of motor movement plays an important role in learning of complex motor skills, from learning to serve in tennis to perfecting a pirouette in ballet. What and where are the neural substrates that underlie motor imagery-based learning? We measured electrocorticographic cortical surface potentials in eight human subjects during overt action and kinesthetic imagery of the same movement, focusing on power in "high frequency" (76-100 Hz) and "low frequency" (8-32 Hz) ranges. We quantitatively establish that the spatial distribution of local neuronal population activity during motor imagery mimics the spatial distribution of activity during actual motor movement. By comparing responses to electrocortical stimulation with imagery-induced cortical surface activity, we demonstrate the role of primary motor areas in movement imagery. The magnitude of imagery-induced cortical activity change was approximately 25% of that associated with actual movement. However, when subjects learned to use this imagery to control a computer cursor in a simple feedback task, the imagery-induced activity change was significantly augmented, even exceeding that of overt movement.

%B Proc Natl Acad Sci U S A %V 107 %P 4430-5 %8 03/2010 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/20160084 %N 9 %R 10.1073/pnas.0913697107 %0 Journal Article %J J Neurosci Methods %D 2008 %T Extraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics. %A Sanchez, Justin C %A Gunduz, Aysegul %A Carney, Paul R %A Principe, Jose %K Adolescent %K Biofeedback, Psychology %K Brain Mapping %K Cerebral Cortex %K Electroencephalography %K Epilepsies, Partial %K Female %K Hand %K Humans %K Magnetic Resonance Imaging %K Physical Therapy Modalities %K Psychomotor Performance %K Signal Processing, Computer-Assisted %K Spectrum Analysis %K User-Computer Interface %X

Electrocorticogram (ECoG) recordings for neuroprosthetics provide a mesoscopic level of abstraction of brain function between microwire single neuron recordings and the electroencephalogram (EEG). Single-trial ECoG neural interfaces require appropriate feature extraction and signal processing methods to identify and model in real-time signatures of motor events in spontaneous brain activity. Here, we develop the clinical experimental paradigm and analysis tools to record broadband (1Hz to 6kHz) ECoG from patients participating in a reaching and pointing task. Motivated by the significant role of amplitude modulated rate coding in extracellular spike based brain-machine interfaces (BMIs), we develop methods to quantify spatio-temporal intermittent increased ECoG voltages to determine if they provide viable control inputs for ECoG neural interfaces. This study seeks to explore preprocessing modalities that emphasize amplitude modulation across frequencies and channels in the ECoG above the level of noisy background fluctuations in order to derive the commands for complex, continuous control tasks. Preliminary experiments show that it is possible to derive online predictive models and spatially localize the generation of commands in the cortex for motor tasks using amplitude modulated ECoG.

%B J Neurosci Methods %V 167 %P 63-81 %8 01/2008 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/17582507 %N 1 %R 10.1016/j.jneumeth.2007.04.019 %0 Journal Article %J Epilepsy Behav %D 2008 %T Voluntary brain regulation and communication with electrocorticogram signals. %A Hinterberger, T. %A Widman, Guido %A Lal, T.N %A Jeremy Jeremy Hill %A Tangermann, Michael %A Rosenstiel, W. %A Schölkopf, B %A Elger, Christian %A Niels Birbaumer %K Adult %K Biofeedback, Psychology %K Cerebral Cortex %K Communication Aids for Disabled %K Dominance, Cerebral %K Electroencephalography %K Epilepsies, Partial %K Female %K Humans %K Imagination %K Male %K Middle Aged %K Motor Activity %K Motor Cortex %K Signal Processing, Computer-Assisted %K Software %K Somatosensory Cortex %K Theta Rhythm %K User-Computer Interface %K Writing %X

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.

%B Epilepsy Behav %V 13 %P 300-6 %8 08/2008 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/18495541 %N 2 %R 10.1016/j.yebeh.2008.03.014