01326nas a2200265   4500008004100000022001400041245005600055210005400111260000900165300001300174490000900187520048800196653001500684653001000699653002400709653002100733653003100754653001900785653003100804653003200835653004100867653002800908100001900936856010500955       2009                        eng d  a1557-170X00aEffective brain-computer interfacing using BCI2000.0 aEffective braincomputer interfacing using BCI2000  c2009  a5498-5010 v20093 aTo facilitate research and development in Brain-Computer Interface (BCI) research, we have been developing a general-purpose BCI system, called BCI2000, over the past nine years. This system has enjoyed a growing adoption in BCI and related areas and has been the basis for some of the most impressive studies reported to date. This paper gives an update on the status of this project by describing the principles of the BCI2000 system, its benefits, and impact on the field to date.10aAlgorithms10aBrain10aElectrocardiography10aEquipment Design10aEquipment Failure Analysis10aRehabilitation10aReproducibility of Results10aSensitivity and Specificity10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aSchalk, Gerwin  uhttps://www.neurotechcenter.org/publications/2009/effective-brain-computer-interfacing-using-bci200002708nas a2200289   4500008004100000022001400041245010300055210006900158260001200227300001600239490000700255520184500262653002602107653001302133653001502146653002502161653001802186653001902204653002702223100001302250700002102263700002102284700001902305700002502324700002102349856004802370       2005                        eng d  a1529-240100aThe interaction of a new motor skill and an old one: H-reflex conditioning and locomotion in rats.0 ainteraction of a new motor skill and an old one Hreflex conditio  c07/2005  a6898–69060 v253 aNew and old motor skills can interfere with each other or interact in other ways. Because each skill entails a distributed pattern of activity-dependent plasticity, investigation of their interactions is facilitated by simple models. In a well characterized model of simple learning, rats and monkeys gradually change the size of the H-reflex, the electrical analog of the spinal stretch reflex. This study evaluates in normal rats the interactions of this new skill of H-reflex conditioning with the old well established skill of overground locomotion. In rats in which the soleus H-reflex elicited in the conditioning protocol (i.e., the conditioning H-reflex) had been decreased by down-conditioning, the H-reflexes elicited during the stance and swing phases of locomotion (i.e., the locomotor H-reflexes) were also smaller. Similarly, in rats in which the conditioning H-reflex had been increased by up-conditioning, the locomotor H-reflexes were also larger. Soleus H-reflex conditioning did not affect the duration, length, or right/left symmetry of the step cycle. However, the conditioned change in the stance H-reflex was positively correlated with change in the amplitude of the soleus locomotor burst, and the correlation was consistent with current estimates of the contribution of primary afferent input to the burst. Although H-reflex conditioning and locomotion did not interfere with each other, H-reflex conditioning did affect how locomotion was produced: it changed soleus burst amplitude and may have induced compensatory changes in the activity of other muscles. These results illustrate and clarify the subtlety and complexity of skill interactions. They also suggest that H-reflex conditioning might be used to improve the abnormal locomotion produced by spinal cord injury or other disorders of supraspinal control.10aH-reflex conditioning10aLearning10aLocomotion10amemory consolidation10aMotor control10aRehabilitation10aspinal cord plasticity1 aChen, Yi1 aChen, Xiang Yang1 aJakeman, Lyn, B.1 aSchalk, Gerwin1 aStokes, Bradford, T.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1603389902685nas 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/1518887601315nas a2200289   4500008004100000022001400041245006100055210005800116260001200174300001600186490000800202520045900210653003100669653002900700653002700729653002000756653002900776653002800805653001400833653001900847653002400866100001900890700002100909700002600930700002100956856004800977       2000                        eng d  a1388-245700aEEG-based communication: presence of an error potential.0 aEEGbased communication presence of an error potential  c12/2000  a2138–21440 v1113 aEEG-based communication could be a valuable new augmentative communication technology for those with severe motor disabilities. Like all communication methods, it faces the problem of errors in transmission. In the Wadsworth EEG-based brain-computer interface (BCI) system, subjects learn to use mu or beta rhythm amplitude to move a cursor to targets on a computer screen. While cursor movement is highly accurate in trained subjects, it is not perfect.10aaugmentative communication10abrain-computer interface10aElectroencephalography10aerror potential10aerror related negativity10aevent related potential10amu rhythm10aRehabilitation10asensorimotor cortex1 aSchalk, Gerwin1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.1 aPfurtscheller, G  uhttp://www.ncbi.nlm.nih.gov/pubmed/11090763