01606nas 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/2040378102198nas a2200229 4500008004100000022001400041245009000055210006900145260001200214520150500226653001001731653001601741653001501757653002701772653001101799653002801810100002001838700001901858700001901877700002401896856004801920 2009 eng d a1940-087X00aUsing an EEG-based brain-computer interface for virtual cursor movement with BCI2000.0 aUsing an EEGbased braincomputer interface for virtual cursor mov c07/20093 a

A brain-computer interface (BCI) functions by translating a neural signal, such as the electroencephalogram (EEG), into a signal that can be used to control a computer or other device. The amplitude of the EEG signals in selected frequency bins are measured and translated into a device command, in this case the horizontal and vertical velocity of a computer cursor. First, the EEG electrodes are applied to the user s scalp using a cap to record brain activity. Next, a calibration procedure is used to find the EEG electrodes and features that the user will learn to voluntarily modulate to use the BCI. In humans, the power in the mu (8-12 Hz) and beta (18-28 Hz) frequency bands decrease in amplitude during a real or imagined movement. These changes can be detected in the EEG in real-time, and used to control a BCI ([1],[2]). Therefore, during a screening test, the user is asked to make several different imagined movements with their hands and feet to determine the unique EEG features that change with the imagined movements. The results from this calibration will show the best channels to use, which are configured so that amplitude changes in the mu and beta frequency bands move the cursor either horizontally or vertically. In this experiment, the general purpose BCI system BCI2000 is used to control signal acquisition, signal processing, and feedback to the user [3].

10aBrain10aCalibration10aElectrodes10aElectroencephalography10aHumans10aUser-Computer Interface1 aWilson, Adam, J1 aSchalk, Gerwin1 aWalton, Léo M1 aWilliams, Justin, C uhttp://www.ncbi.nlm.nih.gov/pubmed/1964147902662nas a2200409 4500008004100000022001400041245008400055210006900139260001200208300001000220490000600230520153900236653001501775653001001790653001801800653003701818653002001855653002401875653002601899653002701925653001301952653001101965653001101976653000901987653001301996653002802009100001902037700001602056700002602072700002002098700001602118700001702134700001302151700002102164700001902185856004802204 2008 eng d a1741-256000aTwo-dimensional movement control using electrocorticographic signals in humans.0 aTwodimensional movement control using electrocorticographic sign c03/2008 a75-840 v53 a

We show here that a brain-computer interface (BCI) using electrocorticographic activity (ECoG) and imagined or overt motor tasks enables humans to control a computer cursor in two dimensions. Over a brief training period of 12-36 min, each of five human subjects acquired substantial control of particular ECoG features recorded from several locations over the same hemisphere, and achieved average success rates of 53-73% in a two-dimensional four-target center-out task in which chance accuracy was 25%. Our results support the expectation that ECoG-based BCIs can combine high performance with technical and clinical practicality, and also indicate promising directions for further research.

10aAdolescent10aAdult10aBrain Mapping10aData Interpretation, Statistical10aDrug Resistance10aElectrocardiography10aElectrodes, Implanted10aElectroencephalography10aEpilepsy10aFemale10aHumans10aMale10aMovement10aUser-Computer Interface1 aSchalk, Gerwin1 aMiller, K J1 aAnderson, Nicholas, R1 aWilson, Adam, J1 aSmyth, Matt1 aOjemann, J G1 aMoran, D1 aWolpaw, Jonathan1 aLeuthardt, E C uhttp://www.ncbi.nlm.nih.gov/pubmed/1831081302202nas a2200373 4500008004100000022001400041245007800055210006900133260001200202300001100214490000700225520114300232653001001375653001801385653002001403653003601423653002501459653002201484653001101506653001101517653001601528653000901544653002401553653002701577653002401604653002801628653001301656100002001669700002501689700002301714700001901737700002401756856004801780 2006 eng d a1534-432000aECoG factors underlying multimodal control of a brain-computer interface.0 aECoG factors underlying multimodal control of a braincomputer in c06/2006 a246-500 v143 a

Most current brain-computer interface (BCI) systems for humans use electroencephalographic activity recorded from the scalp, and may be limited in many ways. Electrocorticography (ECoG) is believed to be a minimally-invasive alternative to electroencephalogram (EEG) for BCI systems, yielding superior signal characteristics that could allow rapid user training and faster communication rates. In addition, our preliminary results suggest that brain regions other than the sensorimotor cortex, such as auditory cortex, may be trained to control a BCI system using similar methods as those used to train motor regions of the brain. This could prove to be vital for users who have neurological disease, head trauma, or other conditions precluding the use of sensorimotor cortex for BCI control.

10aAdult10aBrain Mapping10aCerebral Cortex10aCommunication Aids for Disabled10aComputer Peripherals10aEvoked Potentials10aFemale10aHumans10aImagination10aMale10aMan-Machine Systems10aNeuromuscular Diseases10aSystems Integration10aUser-Computer Interface10aVolition1 aWilson, Adam, J1 aFelton, Elizabeth, A1 aGarell, Charles, P1 aSchalk, Gerwin1 aWilliams, Justin, C uhttp://www.ncbi.nlm.nih.gov/pubmed/16792305