02198nas 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/1964147903382nas a2200349 4500008004100000022001400041245008900055210006900144260001200213300001100225490000700236520236800243653002202611653001502633653001202648653003502660653001602695653002402711653002602735653002102761653004002782653002702822653001002849653002502859653002002884100001702904700001902921700001402940700001702954700001402971856004702985 1997 eng d a0090-696400aTechnical features of a CCD video camera system to record cardiac fluorescence data.0 aTechnical features of a CCD video camera system to record cardia c07/1997 a713-250 v253 a

A charge-coupled device (CCD) camera was used to acquire movies of transmembrane activity from thin slices of sheep ventricular epicardial muscle stained with a voltage-sensitive dye. Compared with photodiodes, CCDs have high spatial resolution, but low temporal resolution. Spatial resolution in our system ranged from 0.04 to 0.14 mm/pixel; the acquisition rate was 60, 120, or 240 frames/sec. Propagating waves were readily visualized after subtraction of a background image. The optical signal had an amplitude of 1 to 6 gray levels, with signal-to-noise ratios between 1.5 and 4.4. Because CCD cameras integrate light over the frame interval, moving objects, including propagating waves, are blurred in the resulting movies. A computer model of such an integrating imaging system was developed to study the effects of blur, noise, filtering, and quantization on the ability to measure conduction velocity and action potential duration (APD). The model indicated that blurring, filtering, and quantization do not affect the ability to localize wave fronts in the optical data (i.e., no systematic error in determining spatial position), but noise does increase the uncertainty of the measurements. The model also showed that the low frame rates of the CCD camera introduced a systematic error in the calculation of APD: for cutoff levels > 50%, the APD was erroneously long. Both noise and quantization increased the uncertainty in the APD measurements. The optical measures of conduction velocity were not significantly different from those measured simultaneously with microelectrodes. Optical APDs, however, were longer than the electrically recorded APDs. This APD error could be reduced by using the 50% cutoff level and the fastest frame rate possible.

10aAction Potentials10aAlgorithms10aAnimals10aBody Surface Potential Mapping10aCalibration10aComputer Simulation10aElectric Conductivity10aFluorescent Dyes10aImage Processing, Computer-Assisted10aModels, Cardiovascular10aSheep10aVentricular Function10aVideo Recording1 aBaxter, Bill1 aDavidenko, J M1 aLoew, L M1 aWuskell, J P1 aJalife, J uhttp://www.ncbi.nlm.nih.gov/pubmed/9236983