03279nas a2200337 4500008004100000022001400041245011400055210006900169260001200238300001200250490000700262520231800269653001502587653001002602653001002612653001802622653002702640653001102667653001102678653000902689653001602698653004102714653002002755100001802775700001902793700002202812700001902834700002102853700001902874856004802893 2011 eng d a1095-957200aSpatiotemporal dynamics of electrocorticographic high gamma activity during overt and covert word repetition.0 aSpatiotemporal dynamics of electrocorticographic high gamma acti c02/2011 a2960-720 v543 a

Language is one of the defining abilities of humans. Many studies have characterized the neural correlates of different aspects of language processing. However, the imaging techniques typically used in these studies were limited in either their temporal or spatial resolution. Electrocorticographic (ECoG) recordings from the surface of the brain combine high spatial with high temporal resolution and thus could be a valuable tool for the study of neural correlates of language function. In this study, we defined the spatiotemporal dynamics of ECoG activity during a word repetition task in nine human subjects. ECoG was recorded while each subject overtly or covertly repeated words that were presented either visually or auditorily. ECoG amplitudes in the high gamma (HG) band confidently tracked neural changes associated with stimulus presentation and with the subject's verbal response. Overt word production was primarily associated with HG changes in the superior and middle parts of temporal lobe, Wernicke's area, the supramarginal gyrus, Broca's area, premotor cortex (PMC), primary motor cortex. Covert word production was primarily associated with HG changes in superior temporal lobe and the supramarginal gyrus. Acoustic processing from both auditory stimuli as well as the subject's own voice resulted in HG power changes in superior temporal lobe and Wernicke's area. In summary, this study represents a comprehensive characterization of overt and covert speech using electrophysiological imaging with high spatial and temporal resolution. It thereby complements the findings of previous neuroimaging studies of language and thus further adds to current understanding of word processing in humans.

10aAdolescent10aAdult10aBrain10aBrain Mapping10aElectroencephalography10aFemale10aHumans10aMale10aMiddle Aged10aSignal Processing, Computer-Assisted10aVerbal Behavior1 aPei, Xiao-Mei1 aLeuthardt, E C1 aGaona, Charles, M1 aBrunner, Peter1 aWolpaw, Jonathan1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2102978401606nas a2200301 4500008004100000022001400041245007000055210006600125260001200191300001200203490000700215520071500222653001000937653002100947653002700968653002200995653001101017653002501028653003101053653004101084653001701125653002801142100002001170700002301190700001901213700002401232856004801256 2010 eng d a1558-253100aA procedure for measuring latencies in brain-computer interfaces.0 aprocedure for measuring latencies in braincomputer interfaces c06/2010 a1785-970 v573 a

Brain-computer interface (BCI) systems must process neural signals with consistent timing in order to support adequate system performance. Thus, it is important to have the capability to determine whether a particular BCI configuration (i.e., hardware and software) provides adequate timing performance for a particular experiment. This report presents a method of measuring and quantifying different aspects of system timing in several typical BCI experiments across a range of settings, and presents comprehensive measures of expected overall system latency for each experimental configuration.

10aBrain10aComputer Systems10aElectroencephalography10aEvoked Potentials10aHumans10aModels, Neurological10aReproducibility of Results10aSignal Processing, Computer-Assisted10aTime Factors10aUser-Computer Interface1 aWilson, Adam, J1 aMellinger, Jürgen1 aSchalk, Gerwin1 aWilliams, Justin, C uhttp://www.ncbi.nlm.nih.gov/pubmed/2040378101322nas a2200265 4500008004100000022001400041245005600055210005400111260000900165300001300174490000900187520048800196653001500684653001000699653002400709653002100733653003100754653001900785653003100804653003200835653004100867653002800908100001900936856010100955 2009 eng d a1557-170X00aEffective brain-computer interfacing using BCI2000.0 aEffective braincomputer interfacing using BCI2000 c2009 a5498-5010 v20093 aTo facilitate research and development in Brain-Computer Interface (BCI) research, we have been developing a general-purpose BCI system, called BCI2000, over the past nine years. This system has enjoyed a growing adoption in BCI and related areas and has been the basis for some of the most impressive studies reported to date. This paper gives an update on the status of this project by describing the principles of the BCI2000 system, its benefits, and impact on the field to date.10aAlgorithms10aBrain10aElectrocardiography10aEquipment Design10aEquipment Failure Analysis10aRehabilitation10aReproducibility of Results10aSensitivity and Specificity10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aSchalk, Gerwin uhttps://neurotechcenter.org/publications/2009/effective-brain-computer-interfacing-using-bci200002156nas a2200337 4500008004100000022001400041245008300055210006900138260001200207300000700219490000700226520120000233653001001433653002001443653001101463653002401474653001701498653001301515653002301528653002401551653002801575653001301603653004101616653002801657100001901685700001901704700001801723700001601741700001301757856004801770 2009 eng d a1092-068400aEvolution of brain-computer interfaces: going beyond classic motor physiology.0 aEvolution of braincomputer interfaces going beyond classic motor c07/2009 aE40 v273 a

The notion that a computer can decode brain signals to infer the intentions of a human and then enact those intentions directly through a machine is becoming a realistic technical possibility. These types of devices are known as brain-computer interfaces (BCIs). The evolution of these neuroprosthetic technologies could have significant implications for patients with motor disabilities by enhancing their ability to interact and communicate with their environment. The cortical physiology most investigated and used for device control has been brain signals from the primary motor cortex. To date, this classic motor physiology has been an effective substrate for demonstrating the potential efficacy of BCI-based control. However, emerging research now stands to further enhance our understanding of the cortical physiology underpinning human intent and provide further signals for more complex brain-derived control. In this review, the authors report the current status of BCIs and detail the emerging research trends that stand to augment clinical applications in the future.

10aBrain10aCerebral Cortex10aHumans10aMan-Machine Systems10aMotor Cortex10aMovement10aMovement Disorders10aNeuronal Plasticity10aProstheses and Implants10aResearch10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aLeuthardt, E C1 aSchalk, Gerwin1 aRoland, Jarod1 aRouse, Adam1 aMoran, D uhttp://www.ncbi.nlm.nih.gov/pubmed/1956989202591nas a2200433 4500008004100000022001400041245012500055210006900180260001200249300001100261490000700272520133300279653001001612653001801622653002001640653002501660653002601685653002701711653001301738653001101751653001101762653000901773653001601782653003301798653004101831653001601872100001901888700001801907700002201925700002201947700002001969700002401989700002302013700002002036700001902056700001502075700001902090856004802109 2009 eng d a1525-506900aA practical procedure for real-time functional mapping of eloquent cortex using electrocorticographic signals in humans.0 apractical procedure for realtime functional mapping of eloquent  c07/2009 a278-860 v153 a

Functional mapping of eloquent cortex is often necessary prior to invasive brain surgery, but current techniques that derive this mapping have important limitations. In this article, we demonstrate the first comprehensive evaluation of a rapid, robust, and practical mapping system that uses passive recordings of electrocorticographic signals. This mapping procedure is based on the BCI2000 and SIGFRIED technologies that we have been developing over the past several years. In our study, we evaluated 10 patients with epilepsy from four different institutions and compared the results of our procedure with the results derived using electrical cortical stimulation (ECS) mapping. The results show that our procedure derives a functional motor cortical map in only a few minutes. They also show a substantial concurrence with the results derived using ECS mapping. Specifically, compared with ECS maps, a next-neighbor evaluation showed no false negatives, and only 0.46 and 1.10% false positives for hand and tongue maps, respectively. In summary, we demonstrate the first comprehensive evaluation of a practical and robust mapping procedure that could become a new tool for planning of invasive brain surgeries.

10aAdult10aBrain Mapping10aCerebral Cortex10aElectric Stimulation10aElectrodes, Implanted10aElectroencephalography10aEpilepsy10aFemale10aHumans10aMale10aMiddle Aged10aPractice Guidelines as Topic10aSignal Processing, Computer-Assisted10aYoung Adult1 aBrunner, Peter1 aRitaccio, A L1 aLynch, Timothy, M1 aEmrich, Joseph, F1 aWilson, Adam, J1 aWilliams, Justin, C1 aAarnoutse, Erik, J1 aRamsey, Nick, F1 aLeuthardt, E C1 aBischof, H1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/1936663805137nas a2200373 4500008004100000022001400041245007500055210006900130260001200199300001000211490000800221520406800229653001004297653001504307653001004322653001804332653002404350653002704374653001104401653000904412653002404421653002404445653001904469653003604488653004104524653002404565653002804589100001904617700001904636700002404655700001504679700002104694856004804715 2008 eng d a0165-027000aBrain-computer interfaces (BCIs): Detection Instead of Classification.0 aBraincomputer interfaces BCIs Detection Instead of Classificatio c01/2008 a51-620 v1673 a

Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer through brain-computer interfaces (BCIs). These devices operate by recording signals from the brain and translating these signals into device commands. They can be used by people who are severely paralyzed to communicate without any use of muscle activity. One of the major impediments in translating this novel technology into clinical applications is the current requirement for preliminary analyses to identify the brain signal features best suited for communication. This paper introduces and validates signal detection, which does not require such analysis procedures, as a new concept in BCI signal processing. This detection concept is realized with Gaussian mixture models (GMMs) that are used to model resting brain activity so that any change in relevant brain signals can be detected. It is implemented in a package called SIGFRIED (SIGnal modeling For Real-time Identification and Event Detection). The results indicate that SIGFRIED produces results that are within the range of those achieved using a common analysis strategy that requires preliminary identification of signal features. They indicate that such laborious analysis procedures could be replaced by merely recording brain signals during rest. In summary, this paper demonstrates how SIGFRIED could be used to overcome one of the present impediments to translation of laboratory BCI demonstrations into clinically practical applications.

10aAdult10aAlgorithms10aBrain10aBrain Mapping10aElectrocardiography10aElectroencephalography10aHumans10aMale10aMan-Machine Systems10aNormal Distribution10aOnline Systems10aSignal Detection, Psychological10aSignal Processing, Computer-Assisted10aSoftware Validation10aUser-Computer Interface1 aSchalk, Gerwin1 aBrunner, Peter1 aGerhardt, Lester, A1 aBischof, H1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1792013404391nas a2200409 4500008004100000022001400041245010700055210006900162260001200231300002900243490000700272520323000279653001003509653002203519653001803541653002503559653002603584653002703610653001103637653000903648653001103657653000903668653001603677653001703693653001703710653004103727653001103768100001903779700002003798700002603818700001903844700002003863700002003883700001303903700001703916856004803933 2007 eng d a1524-404000aElectrocorticographic Frequency Alteration Mapping: A Clinical Technique for Mapping the Motor Cortex.0 aElectrocorticographic Frequency Alteration Mapping A Clinical Te c04/2007 a260-70; discussion 270-10 v603 a

OBJECTIVE: 

Electrocortical stimulation (ECS) has been well established for delineating the eloquent cortex. However, ECS is still coarse and inefficient in delineating regions of the functional cortex and can be hampered by after-discharges. Given these constraints, an adjunct approach to defining the motor cortex is the use of electrocorticographic signal changes associated with active regions of the cortex. The broad range of frequency oscillations are categorized into two main groups with respect to the sensorimotor cortex: low and high frequency bands. The low frequency bands tend to show a power reduction with cortical activation, whereas the high frequency bands show power increases. These power changes associated with the activated cortex could potentially provide a powerful tool in delineating areas of the motor cortex. We explore electrocorticographic signal alterations as they occur with activated regions of the motor cortex, as well as its potential in clinical brain mapping applications.

METHODS: 

We evaluated seven patients who underwent invasive monitoring for seizure localization. Each patient had extraoperative ECS mapping to identify the motor cortex. All patients also performed overt hand and tongue motor tasks to identify associated frequency power changes in regard to location and degree of concordance with ECS results that localized either hand or tongue motor function.

RESULTS: 

The low frequency bands had a high sensitivity (88.9-100%) and a lower specificity (79.0-82.6%) for identifying electrodes with either hand or tongue ECS motor responses. The high frequency bands had a lower sensitivity (72.7-88.9%) and a higher specificity (92.4-94.9%) in correlation with the same respective ECS positive electrodes.

CONCLUSION: 

The concordance between stimulation and spectral power changes demonstrate the possible utility of electrocorticographic frequency alteration mapping as an adjunct method to improve the efficiency and resolution of identifying the motor cortex.

10aAdult10aBiological Clocks10aBrain Mapping10aElectric Stimulation10aElectrodes, Implanted10aElectroencephalography10aFemale10aHand10aHumans10aMale10aMiddle Aged10aMotor Cortex10aOscillometry10aSignal Processing, Computer-Assisted10aTongue1 aLeuthardt, E C1 aMiller, John, W1 aAnderson, Nicholas, R1 aSchalk, Gerwin1 aDowling, Joshua1 aMiller, John, W1 aMoran, D1 aOjemann, J G uhttp://www.ncbi.nlm.nih.gov/pubmed/1741516203540nas a2200457 4500008004100000022001400041245004900055210004100104260001200145300001100157490000700168520234400175653001002519653001502529653001402544653001002558653002702568653002702595653002102622653001302643653001102656653000902667653000902676653001902685653001102704653003102715653002702746653000902773653001302782653003302795653004102828653002802869100002302897700001902920700002102939700002002960700001802980700002102998700001503019856004803034 2007 eng d a1053-811900aAn MEG-based brain-computer interface (BCI).0 aMEGbased braincomputer interface BCI c07/2007 a581-930 v363 a

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.

10aAdult10aAlgorithms10aArtifacts10aBrain10aElectroencephalography10aElectromagnetic Fields10aElectromyography10aFeedback10aFemale10aFoot10aHand10aHead Movements10aHumans10aMagnetic Resonance Imaging10aMagnetoencephalography10aMale10aMovement10aPrincipal Component Analysis10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aMellinger, Jürgen1 aSchalk, Gerwin1 aBraun, Christoph1 aPreissl, Hubert1 aRosenstiel, W1 aBirbaumer, Niels1 aKübler, A uhttp://www.ncbi.nlm.nih.gov/pubmed/1747551102917nas a2200265 4500008004100000022001400041245009700055210006900152260001200221300001000233490000800243520213200251653002202383653001202405653002102417653001302438653001802451653002102469653000902490653004102499100001902540700002302559700002102582856004802603 2002 eng d a0165-027000aTemporal transformation of multiunit activity improves identification of single motor units.0 aTemporal transformation of multiunit activity improves identific c02/2002 a87-980 v1143 a

This report describes a temporally based method for identifying repetitive firing of motor units. This approach is ideally suited to spike trains with negative serially correlated inter-spike intervals (ISIs). It can also be applied to spike trains in which ISIs exhibit little serial correlation if their coefficient of variation (COV) is sufficiently low. Using a novel application of the Hough transform, this method (i.e. the modified Hough transform (MHT)) maps motor unit action potential (MUAP) firing times into a feature space with ISI and offset (defined as the latency from an arbitrary starting time to the first MUAP in the train) as dimensions. Each MUAP firing time corresponds to a pattern in the feature space that represents all possible MUAP trains with a firing at that time. Trains with stable ISIs produce clusters in the feature space, whereas randomly firing trains do not. The MHT provides a direct estimate of mean firing rate and its variability for the entire data segment, even if several individual MUAPs are obscured by firings from other motor units. Addition of this method to a shape-based classification approach markedly improved rejection of false positives using simulated data and identified spike trains in whole muscle electromyographic recordings from rats. The relative independence of the MHT from the need to correctly classify individual firings permits a global description of stable repetitive firing behavior that is complementary to shape-based approaches to MUAP classification.

10aAction Potentials10aAnimals10aElectromyography10aH-Reflex10aMotor Neurons10aMuscle, Skeletal10aRats10aSignal Processing, Computer-Assisted1 aSchalk, Gerwin1 aCarp, Jonathan, S.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/1185004303091nas a2200373 4500008004100000022001400041245008600055210006900141260001200210300001100222490000600233520197800239653001502217653002002232653003602252653002102288653002702309653002202336653001102358653002702369653004102396653002802437100002102465700002102486700001902507700002602526700001702552700001902569700002102588700001802609700001802627700002402645856004802669 2000 eng d a1063-652800aBrain-computer interface technology: a review of the first international meeting.0 aBraincomputer interface technology a review of the first interna c06/2000 a164-730 v83 a

Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.

10aAlgorithms10aCerebral Cortex10aCommunication Aids for Disabled10aDisabled Persons10aElectroencephalography10aEvoked Potentials10aHumans10aNeuromuscular Diseases10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aWolpaw, Jonathan1 aBirbaumer, Niels1 aHeetderks, W J1 aMcFarland, Dennis, J.1 aPeckham, P H1 aSchalk, Gerwin1 aDonchin, Emanuel1 aQuatrano, L A1 aRobinson, C J1 aVaughan, Theresa, M uhttp://www.ncbi.nlm.nih.gov/pubmed/10896178