01656nas a2200337 4500008004100000022001400041245008900055210006900144260001200213300001100225490000700236520066900243653001000912653001800922653003300940653002700973653001101000653003001011653001301041653003601054100001801090700001901108700002701127700002201154700001301176700001901189700002301208700002001231700001901251856004801270 2010 eng d a1525-506900aProceedings of the first international workshop on advances in electrocorticography.0 aProceedings of the first international workshop on advances in e c10/2010 a204-150 v193 a

In October 2009, a group of neurologists, neurosurgeons, computational neuroscientists, and engineers congregated to present novel developments transforming human electrocorticography (ECoG) beyond its established relevance in clinical epileptology. The contents of the proceedings advanced the role of ECoG in seizure detection and prediction, neurobehavioral research, functional mapping, and brain-computer interface technology. The meeting established the foundation for future work on the methodology and application of surface brain recordings.

10aBrain10aBrain Mapping10aDiagnosis, Computer-Assisted10aElectroencephalography10aHumans10aInternational Cooperation10aSeizures10aSignal Detection, Psychological1 aRitaccio, A L1 aBrunner, Peter1 aCervenka, Mackenzie, C1 aCrone, Nathan, E.1 aGuger, C1 aLeuthardt, E C1 aOostenveld, Robert1 aStacey, William1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2088938405137nas 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/17920134