@article {3091, title = {Should the parameters of a BCI translation algorithm be continually adapted?.}, journal = {Journal of neuroscience methods}, volume = {199}, year = {2011}, month = {07/2011}, pages = {103{\textendash}107}, abstract = {People with or without motor disabilities can learn to control sensorimotor rhythms (SMRs) recorded from the scalp to move a computer cursor in one or more dimensions or can use the P300 event-related potential as a control signal to make discrete selections. Data collected from individuals using an SMR-based or P300-based BCI were evaluated offline to estimate the impact on performance of continually adapting the parameters of the translation algorithm during BCI operation. The performance of the SMR-based BCI was enhanced by adaptive updating of the feature weights or adaptive normalization of the features. In contrast, P300 performance did not benefit from either of these procedures.}, keywords = {adaptation, brain-computer interface, EEG}, issn = {1872-678X}, doi = {10.1016/j.jneumeth.2011.04.037}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21571004}, author = {Dennis J. McFarland and Sarnacki, William A. and Jonathan Wolpaw} } @article {3133, title = {Brain-computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms.}, journal = {Progress in brain research}, volume = {159}, year = {2006}, month = {02/2006}, pages = {411{\textendash}419}, abstract = {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.}, keywords = {adaptation, BCI, Signal Processing}, issn = {0079-6123}, doi = {10.1016/S0079-6123(06)59026-0}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17071245}, author = {Dennis J. McFarland and Krusienski, Dean J. and Jonathan Wolpaw} }