TY - JOUR T1 - BCI Meeting 2005–workshop on BCI signal processing: feature extraction and translation. JF - IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Y1 - 2006 A1 - Dennis J. McFarland A1 - Anderson, Charles W. A1 - Müller, Klaus-Robert A1 - Schlögl, Alois A1 - Krusienski, Dean J. KW - Brain-computer interface (BCI) KW - prediction KW - Signal Processing AB - This 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. VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792278 ER - TY - JOUR T1 - Brain-computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms. JF - Progress in brain research Y1 - 2006 A1 - Dennis J. McFarland A1 - Krusienski, Dean J. A1 - Jonathan Wolpaw KW - adaptation KW - BCI KW - Signal Processing AB - The 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. VL - 159 UR - http://www.ncbi.nlm.nih.gov/pubmed/17071245 ER -