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/17071245