01769nas a2200373   4500008004100000245003800041210003700079260001200116300000900128490000600137520083700143653000800980653002900988653001601017100001801033700001601051700001501067700002101082700002101103700002101124700001201145700001501157700001601172700002001188700001301208700002101221700001501242700001901257700001601276700001601292700001501308700002401323856004801347       2012                        eng d00aReview of the BCI Competition IV.0 aReview of the BCI Competition IV  c07/2012  a1-310 v63 aThe BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students. The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) session-to-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.10aBCI10abrain-computer interface10acompetition1 aTangermann, M1 aMuller, K R1 aAertsen, A1 aBirbaumer, Niels1 aBraun, Christoph1 aBrunner, Clemens1 aLeeb, R1 aMehring, C1 aMiller, K J1 aMueller-Putz, G1 aNolte, G1 aPfurtscheller, G1 aPreissl, H1 aSchalk, Gerwin1 aSchlögl, A1 aVidaurre, C1 aWaldert, S1 aBlankertz, Benjamin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2281165701315nas 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/1109076301936nas a2200205   4500008004100000022001400041245007300055210006900128260001200197300001400209490000600223520132600229653002201555653002201577100002101599700001601620700002601636700002101662856004701683       1998                        eng d  a1063-652800aEEG-based communication: improved accuracy by response verification.0 aEEGbased communication improved accuracy by response verificatio  c09/1998  a326–3330 v63 aHumans can learn to control the amplitude of electroencephalographic (EEG) activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. EEG-based communication could provide a new augmentative communication channel for individuals with motor disabilities. In the present system, each dimension of cursor movement is controlled by a linear equation. While the intercept in the equation is continually updated, it does not perfectly eliminate the impact of spontaneous variations in EEG amplitude. This imperfection reduces the accuracy of cursor movement. We evaluated a response verification (RV) procedure in which each outcome is determined by two opposite trials (e.g., one top-target trial and one bottom-target trial). Success, or failure, on both is required for a definitive outcome. The RV procedure reduces errors due to imperfection in intercept selection. Accuracy for opposite-trial pairs exceeds that predicted from the accuracies of individual trials, and greatly exceeds that for same-trial pairs. The RV procedure should be particularly valuable when the first trial has >2 possible targets, because the second trial need only confirm or deny the outcome of the first, and it should be applicable to nonlinear as well as to linear algorithms.10aComputer-Assisted10aSignal Processing1 aWolpaw, Jonathan1 aRamoser, H.1 aMcFarland, Dennis, J.1 aPfurtscheller, G  uhttp://www.ncbi.nlm.nih.gov/pubmed/974991002392nas a2200181   4500008004100000022001400041245008200055210006900137260001200206300001400218490000700232520184100239653002502080100001602105700002102121700002102142856004702163       1997                        eng d  a0013-558500aEEG-based communication: evaluation of alternative signal prediction methods.0 aEEGbased communication evaluation of alternative signal predicti  c09/1997  a226–2330 v423 aIndividuals can learn to control the amplitude of EEG activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. For one-dimensional (i.e., vertical) cursor movement, a linear equation translates the EEG activity into cursor movement. To translate an individual's EEG control into cursor control as effectively as possible, the intercept in this equation, which determines whether upward or downward movement occurs, should be set so that top and bottom targets are equally accessible. The present study compares alternative methods for using an individual's previous performance to select the intercept for subsequent trials. In offline analyses, five different intercept selection methods were applied to EEG data collected while trained subjects were moving the cursor to targets at the top or bottom edge of the screen. In the first two methods-moving average, and weighted sum-a single intercept was selected for the entire 1-2 sec period of each trial. In the other three methods-blocked moving average, blocked weighted sum, and blocked recursive sum (a variation of the weighted sum)-an intercept was selected for each 200-ms segment of the trial. The results from these methods were compared in regard to their balance between upward and downward movements and their consistency of performance across trials. For all subjects combined, the five methods performed similarly. However, performance across subjects was more consistent for the moving average, blocked moving average, and blocked recursive sum methods than for the weighted sum and blocked weighted sum methods. Due to its consistent performance and its computational simplicity, the moving average method, using the five most recent pairs of top and bottom trials, appears to be the method of choice.10aSomatosensory Cortex1 aRamoser, H.1 aWolpaw, Jonathan1 aPfurtscheller, G  uhttp://www.ncbi.nlm.nih.gov/pubmed/934288702490nas a2200265   4500008004100000022001400041245004000055210003800095260001200133300001400145490000700159520177200166653002801938653002701966653001401993653002502007653001502032653001902047653002402066100002102090700001902111700002102130700002602151856004702177       1997                        eng d  a0736-025800aTiming of EEG-based cursor control.0 aTiming of EEGbased cursor control  c11/1997  a529–5380 v143 aRecent studies show that humans can learn to control the amplitude of electroencephalography (EEG) activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. EEG-based communication could be a valuable new communication and control option for those with severe motor disabilities. Realization of this potential requires detailed knowledge of the characteristic features of EEG control. This study examined the course of EEG control after presentation of a target. At the beginning of each trial, a target appeared at the top or bottom edge of the subject's video screen and 1 sec later a cursor began to move vertically as a function of EEG amplitude in a specific frequency band. In well-trained subjects, this amplitude was high at the time the target appeared and then either remained high (i.e., for a top target) or fell rapidly (i.e., for a bottom target). Target-specific EEG amplitude control began 0.5 sec after the target appeared and appeared to wax and wane with a period of approximately 1 sec until the cursor reached the target (i.e., a hit) or the opposite edge of the screen (i.e., a miss). Accuracy was 90% or greater for each subject. Top-target errors usually occurred later in the trial because of failure to reach and/or maintain sufficiently high amplitude, whereas bottom-target errors usually occurred immediately because of failure to reduce an initially high amplitude quickly enough. The results suggest modifications that could improve performance. These include lengthening the intertrial period, shortening the delay between target appearance and cursor movement, and including time within the trial as a variable in the equation that translates EEG into cursor movement.10aassistive communication10aElectroencephalography10amu rhythm10aoperant conditioning10aprosthesis10aRehabilitation10asensorimotor cortex1 aWolpaw, Jonathan1 aFlotzinger, D.1 aPfurtscheller, G1 aMcFarland, Dennis, J.  uhttp://www.ncbi.nlm.nih.gov/pubmed/945806001530nas a2200205   4500008004100000022001400041245011100055210006900166260000900235300001400244490000700258520087900265653002801144100002101172700001901193700001801212700002101230700002601251856004701277       1995                        eng d  a0047-655200aEEG-based brain computer interface (BCI). Search for optimal electrode positions and frequency components.0 aEEGbased brain computer interface BCI Search for optimal electro  c1996  a111–1210 v213 aSeveral laboratories around the world have recently started to investigate EEG-based brain computer interface (BCI) systems in order to create a new communication channel for subjects with severe motor impairments. The present paper describes an initial evaluation of 64-channel EEG data recorded while subjects used one EEG channel over the left sensorimotor area to control on-line vertical cursor movement. Targets were given at the top or bottom of a computer screen. Data from 3 subjects in the early stages of training were analyzed by calculating band power time courses and maps for top and bottom targets separately. In addition, the Distinction Sensitive Learning Vector Quantizer (DSLVQ) was applied to single-trial EEG data. It was found that for each subject there exist optimal electrode positions and frequency components for on-line EEG-based cursor control.10aUser-Computer Interface1 aPfurtscheller, G1 aFlotzinger, D.1 aPregenzer, M.1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.  uhttp://www.ncbi.nlm.nih.gov/pubmed/8776708