02591nas 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 aMany 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