01200nas a2200229   4500008004100000022001400041245009400055210007100149260001200220300001400232490000700246520047400253653003500727653001500762653002200777100002600799700002600825700002600851700002000877700002500897856004800922       2006                        eng d  a1534-432000aBCI Meeting 2005–workshop on BCI signal processing: feature extraction and translation.0 aBCI Meeting 2005–workshop on BCI signal processing feature extra  c06/2006  a135–1380 v143 aThis paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant to brain-computer interface (BCI) feature extraction and translation. The issues discussed include a taxonomy of methods and applications, time-frequency spatial analysis, optimization schemes, the role of insight in analysis, adaptation, and methods for quantifying BCI feedback.10aBrain-computer interface (BCI)10aprediction10aSignal Processing1 aMcFarland, Dennis, J.1 aAnderson, Charles, W.1 aMüller, Klaus-Robert1 aSchlögl, Alois1 aKrusienski, Dean, J.  uhttp://www.ncbi.nlm.nih.gov/pubmed/1679227801505nas 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/1707124501936nas 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/9749910