05137nas 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/1792013403802nas a2200385 4500008004100000022001400041245008700055210006900142260001200211300001000223490000700233520264300240653001502883653001002898653003602908653002302944653002702967653002202994653001103016653002703027653002403054653003803078653002803116100002403144700002603168700002403194700001903218700002103237700002003258700002403278700002203302700002303324700002103347856004803368 2006 eng d a1534-432000aThe BCI competition III: Validating alternative approaches to actual BCI problems.0 aBCI competition III Validating alternative approaches to actual  c06/2006 a153-90 v143 a

brain-computer interface (BCI) is a system that allows its users to control external devices with brainactivity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.

10aAlgorithms10aBrain10aCommunication Aids for Disabled10aDatabases, Factual10aElectroencephalography10aEvoked Potentials10aHumans10aNeuromuscular Diseases10aSoftware Validation10aTechnology Assessment, Biomedical10aUser-Computer Interface1 aBlankertz, Benjamin1 aMüller, Klaus-Robert1 aKrusienski, Dean, J1 aSchalk, Gerwin1 aWolpaw, Jonathan1 aSchlögl, Alois1 aPfurtscheller, Gert1 aMillán, José, R1 aSchröder, Michael1 aBirbaumer, Niels uhttp://www.ncbi.nlm.nih.gov/pubmed/1679228202034nas a2200361 4500008004100000022001400041245010100055210006900156260001200225300001100237490000800248520093900256653001501195653002001210653002801230653005301258653004001311653003101351653001301382653001801395653001401413653002601427653001301453653002401466100001701490700001701507700002101524700001801545700001901563700001901582700002301601856004801624 2005 eng d a1047-847700aParticle picking by segmentation: a comparative study with SPIDER-based manual particle picking.0 aParticle picking by segmentation a comparative study with SPIDER c12/2005 a211-200 v1523 a

Boxing hundreds of thousands of particles in low-dose electron micrographs is one of the major bottle-necks in advancing toward achieving atomic resolution reconstructions of biological macromolecules. We have shown that a combination of pre-processing operations and segmentation can be used as an effective, automatic tool for identifying and boxing single-particle images. This paper provides a brief description of how this method has been applied to a large data set of micrographs of ice-embedded ribosomes, including a comparative analysis of the efficiency of the method. Some results on processing micrographs of tripeptidyl peptidase II particles are also shown. In both cases, we have achieved our goal of selecting at least 80% of the particles that an expert would select with less than 10% false positives.

10aAlgorithms10aAminopeptidases10aCryoelectron Microscopy10aDipeptidyl-Peptidases and Tripeptidyl-Peptidases10aImage Processing, Computer-Assisted10aImaging, Three-Dimensional10aInternet10aParticle Size10aRibosomes10aSerine Endopeptidases10aSoftware10aSoftware Validation1 aAdiga, Umesh1 aBaxter, Bill1 aHall, Richard, J1 aRockel, Beate1 aRath, Bimal, K1 aFrank, Joachim1 aGlaeser, Robert, M uhttp://www.ncbi.nlm.nih.gov/pubmed/16330229