02706nas a2200265   4500008004100000245009100041210006900132260001200201490000700213520195600220653000802176653001802184653001902202653001002221653002402231653001102255100001502266700001802281700001302299700001502312700001802327700002102345700002202366856005202388       2018                        eng d00aControlling pre-movement sensorimotor rhythm can improve finger extension after stroke0 aControlling premovement sensorimotor rhythm can improve finger e  c08/20180 v153 aObjective. Brain–computer interface (BCI) technology is attracting increasing interest as a tool for enhancing recovery of motor function after stroke, yet the optimal way to apply this technology is unknown. Here, we studied the immediate and therapeutic effects of BCI-based training to control pre-movement sensorimotor rhythm (SMR) amplitude on robot-assisted finger extension in people with stroke. Approach. Eight people with moderate to severe hand impairment due to chronic stroke completed a four-week three-phase protocol during which they practiced finger extension with assistance from the FINGER robotic exoskeleton. In Phase 1, we identified spatiospectral SMR features for each person that correlated with the intent to extend the index and/or middle finger(s). In Phase 2, the participants learned to increase or decrease SMR features given visual feedback, without movement. In Phase 3, the participants were cued to increase or decrease their SMR features, and when successful, were then cued to immediately attempt to extend the finger(s) with robot assistance. Main results. Of the four participants that achieved SMR control in Phase 2, three initiated finger extensions with a reduced reaction time after decreasing (versus increasing) pre-movement SMR amplitude during Phase 3. Two also extended at least one of their fingers more forcefully after decreasing pre-movement SMR amplitude. Hand function, measured by the box and block test (BBT), improved by 7.3  ±  7.5 blocks versus 3.5  ±  3.1 blocks in those with and without SMR control, respectively. Higher BBT scores at baseline correlated with a larger change in BBT score. Significance. These results suggest that learning to control person-specific pre-movement SMR features associated with finger extension can improve finger extension ability after stroke for some individuals. These results merit further investigation in a rehabilitation context.10aBCI10aMotor control10aRehabilitation10arobot10asensorimotor rhythm10aStroke1 aNorman, SL1 aMcFarland, DJ1 aMiner, A1 aCramer, SC1 aWolbrecht, ET1 aWolpaw, Jonathan1 aReinkensmeyer, DJ  uhttp://stacks.iop.org/1741-2552/15/i=5/a=05602601741nas a2200253   4500008004100000022001400041245008200055210006900137260001200206300001400218490000700232520097500239653000801214653002901222653002201251653002401273100003201297700002601329700002001355700002401375700001901399700002101418856004801439       2009                        eng d  a1873-624600aA scanning protocol for a sensorimotor rhythm-based brain-computer interface.0 ascanning protocol for a sensorimotor rhythmbased braincomputer i  c02/2009  a169–1750 v803 aThe scanning protocol is a novel brain-computer interface (BCI) implementation that can be controlled with sensorimotor rhythms (SMRs) of the electroencephalogram (EEG). The user views a screen that shows four choices in a linear array with one marked as target. The four choices are successively highlighted for 2.5s each. When a target is highlighted, the user can select it by modulating the SMR. An advantage of this method is the capacity to choose among multiple choices with just one learned SMR modulation. Each of 10 naive users trained for ten 30 min sessions over 5 weeks. User performance improved significantly (p<0.001) over the sessions and ranged from 30 to 80% mean accuracy of the last three sessions (chance accuracy=25%). The incidence of correct selections depended on the target position. These results suggest that, with further improvements, a scanning protocol can be effective. The ultimate goal is to expand it to a large matrix of selections.10aBCI10abrain-computer interface10ascanning protocol10asensorimotor rhythm1 aFriedrich, Elisabeth, V. C.1 aMcFarland, Dennis, J.1 aNeuper, Christa1 aVaughan, Theresa, M1 aBrunner, Peter1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1878660301471nas a2200325   4500008004100000022001400041245006200055210005900117260001200176300001400188490000600202520059000208653000800798653002800806653000800834653000800842653002600850653002900876653002800905653000800933653000800941653002300949653002800972653001001000653001101010100002501021700003001046700002101076856004801097       2007                        eng d  a1743-444000aBrain-computer interface systems: progress and prospects.0 aBraincomputer interface systems progress and prospects  c07/2007  a463–4740 v43 aBrain-computer interface (BCI) systems support communication through direct measures of neural activity without muscle activity. BCIs may provide the best and sometimes the only communication option for users disabled by the most severe neuromuscular disorders and may eventually become useful to less severely disabled and/or healthy individuals across a wide range of applications. This review discusses the structure and functions of BCI systems, clarifies terminology and addresses practical applications. Progress and opportunities in the field are also identified and explicated.10aALS10aassistive communication10aBCI10aBMI10abrain-acuated control10abrain-computer interface10abrain-machine interface10aEEG10aERP10alocked-in syndrome10aslow cortical potential10aSSVEP10aStroke1 aAllison, Brendan, Z.1 aWolpaw, Elizabeth, Winter1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1760568201505nas a2200205   4500008004100000022001400041245010600055210006900161260001200230300001400242490000800256520087000264653001501134653000801149653002201157100002601179700002501205700002101230856004801251       2006                        eng d  a0079-612300aBrain-computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms.0 aBraincomputer interface signal processing at the Wadsworth Cente  c02/2006  a411–4190 v1593 aThe Wadsworth brain-computer interface (BCI), based on mu and beta sensorimotor rhythms, uses one- and two-dimensional cursor movement tasks and relies on user training. This is a real-time closed-loop system. Signal processing consists of channel selection, spatial filtering, and spectral analysis. Feature translation uses a regression approach and normalization. Adaptation occurs at several points in this process on the basis of different criteria and methods. It can use either feedforward (e.g., estimating the signal mean for normalization) or feedback control (e.g., estimating feature weights for the prediction equation). We view this process as the interaction between a dynamic user and a dynamic system that coadapt over time. Understanding the dynamics of this interaction and optimizing its performance represent a major challenge for BCI research.10aadaptation10aBCI10aSignal Processing1 aMcFarland, Dennis, J.1 aKrusienski, Dean, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1707124502685nas a2200433   4500008004100000022001400041245011000055210006900165260001200234300001600246490000700262520140200269653003101671653000801702653001601710653002901726653000801755653000801763653002801771653003601799653001401835653000901849653001901858653003201877653002901909100002401938700002601962700001901988700002402007700001902031700002102050700002002071700002002091700002402111700002402135700002302159700002102182856004802203       2004                        eng d  a0018-929400aThe BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials.0 aBCI Competition 2003 progress and perspectives in detection and   c06/2004  a1044–10510 v513 aInterest in developing a new method of man-to-machine communication–a brain-computer interface (BCI)–has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.10aaugmentative communication10aBCI10abeta-rhythm10abrain-computer interface10aEEG10aERP10aimagined hand movements10alateralized readiness potential10amu-rhythm10aP30010aRehabilitation10asingle-trial classification10aslow cortical potentials1 aBlankertz, Benjamin1 aMüller, Klaus-Robert1 aCurio, Gabriel1 aVaughan, Theresa, M1 aSchalk, Gerwin1 aWolpaw, Jonathan1 aSchlögl, Alois1 aNeuper, Christa1 aPfurtscheller, Gert1 aHinterberger, Thilo1 aSchröder, Michael1 aBirbaumer, Niels  uhttp://www.ncbi.nlm.nih.gov/pubmed/15188876