07041nas a2200313 4500008004100000022001400041245008200055210006900137260001200206300001100218490000600229520615100235653002806386653001006414653001806424653002706442653002106469653003106490653001106521653002406532653001306556653002806569100001906597700001506616700001306631700001606644700001906660856004806679 2011 eng d a1741-255200aCurrent Trends in Hardware and Software for Brain-Computer Interfaces (BCIs).0 aCurrent Trends in Hardware and Software for BrainComputer Interf c04/2011 a0250010 v83 a

brain-computer interface (BCI) provides a non-muscular communication channel to people with and without disabilities. BCI devices consist of hardware and software. BCI hardware records signals from the brain, either invasively or non-invasively, using a series of device components. BCI software then translates these signals into device output commands and provides feedback. One may categorize different types of BCI applications into the following four categories: basic research, clinical/translational research, consumer products, and emerging applications. These four categories use BCI hardware and software, but have different sets of requirements. For example, while basic research needs to explore a wide range of system configurations, and thus requires a wide range of hardware and software capabilities, applications in the other three categories may be designed for relatively narrow purposes and thus may only need a very limited subset of capabilities. This paper summarizes technical aspects for each of these four categories of BCI applications. The results indicate that BCI technology is in transition from isolated demonstrations to systematic research and commercial development. This process requires several multidisciplinary efforts, including the development of better integrated and more robust BCI hardware and software, the definition of standardized interfaces, and the developmentof certification, dissemination and reimbursement procedures.

10aBiofeedback, Psychology10aBrain10aBrain Mapping10aElectroencephalography10aEquipment Design10aEquipment Failure Analysis10aHumans10aMan-Machine Systems10aSoftware10aUser-Computer Interface1 aBrunner, Peter1 aBianchi, L1 aGuger, C1 aCincotti, F1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/2143653607936nas a2200361 4500008004100000022001400041245009600055210006900151260001200220300001100232490000800243520694900251653001507200653001007215653002807225653002007253653001007273653002507283653002407308653001107332653001107343653000907354653001607363653001907379653001607398100001607414700001907430700002207449700001707471700001707488700002107505856004807526 2010 eng d a1091-649000aCortical activity during motor execution, motor imagery, and imagery-based online feedback.0 aCortical activity during motor execution motor imagery and image c03/2010 a4430-50 v1073 a

Imagery of motor movement plays an important role in learning of complex motor skills, from learning to serve in tennis to perfecting a pirouette in ballet. What and where are the neural substrates that underlie motor imagery-based learning? We measured electrocorticographic cortical surface potentials in eight human subjects during overt action and kinesthetic imagery of the same movement, focusing on power in "high frequency" (76-100 Hz) and "low frequency" (8-32 Hz) ranges. We quantitatively establish that the spatial distribution of local neuronal population activity during motor imagery mimics the spatial distribution of activity during actual motor movement. By comparing responses to electrocortical stimulation with imagery-induced cortical surface activity, we demonstrate the role of primary motor areas in movement imagery. The magnitude of imagery-induced cortical activity change was approximately 25% of that associated with actual movement. However, when subjects learned to use this imagery to control a computer cursor in a simple feedback task, the imagery-induced activity change was significantly augmented, even exceeding that of overt movement.

10aAdolescent10aAdult10aBiofeedback, Psychology10aCerebral Cortex10aChild10aElectric Stimulation10aElectrocardiography10aFemale10aHumans10aMale10aMiddle Aged10aMotor Activity10aYoung Adult1 aMiller, K J1 aSchalk, Gerwin1 aFetz, Eberhard, E1 aNijs, Marcel1 aOjemann, J G1 aRao, Rajesh, P N uhttp://www.ncbi.nlm.nih.gov/pubmed/20160084