@article {2187, title = {Real-time detection of event-related brain activity.}, journal = {Neuroimage}, volume = {43}, year = {2008}, month = {11/2008}, pages = {245-9}, abstract = {

The complexity and inter-individual variation of\ brain\ signals impedes real-time detection of events in raw signals. To convert these complex signals into results that can be readily understood, current approaches usually apply statistical methods to data from known conditions after all data have been collected. The capability to provide meaningful visualization of complex\ brain\ signals without the requirement to initially collect data from all conditions would provide a new tool, essentially a new imaging technique, that would open up new avenues for the study of\ brain\ function. Here we show that a new analysis approach, called SIGFRIED, can overcome this serious limitation of current methods. SIGFRIED can visualize\ brain\ signal changes without requiring prior data collection from all conditions. This capacity is particularly well suited to applications in which comprehensive prior data collection is impossible or impractical, such as intraoperative localization of cortical function or detection of epileptic seizures.

}, keywords = {Adult, Algorithms, Brain Mapping, Computer Systems, Diagnosis, Computer-Assisted, Electroencephalography, Epilepsy, Evoked Potentials, Female, Humans, Male, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2008.07.037}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18718544}, author = {Gerwin Schalk and Leuthardt, E C and Peter Brunner and Ojemann, J G and Lester A Gerhardt and Jonathan Wolpaw} } @article {2174, title = {A {\textmu}-rhythm Matched Filter for Continuous Control of a Brain-Computer Interface.}, journal = {IEEE Trans Biomed Eng}, volume = {54}, year = {2007}, month = {02/2007}, pages = {273-80}, abstract = {

A brain-computer interface (BCI) is a system that provides an alternate nonmuscular communication/control channel for individuals with severe neuromuscular disabilities. With proper training, individuals can learn to modulate the amplitude of specific electroencephalographic (EEG) components (e.g., the 8-12 Hz mu rhythm and 18-26 Hz beta rhythm) over the sensorimotor cortex and use them to control a cursor on a computer screen. Conventional spectral techniques for monitoring the\ continuousamplitude fluctuations fail to capture essential amplitude/phase relationships of the mu and beta rhythms in a compact fashion and, therefore, are suboptimal. By extracting the characteristic mu rhythm for a user, the exact morphology can be characterized and exploited as a matched filter. A simple, parameterized model for the characteristic mu rhythm is proposed and its effectiveness as a matched filter is examined online for a one-dimensional cursor control task. The results suggest that amplitude/phase coupling exists between the mu and beta bands during event-related desynchronization, and that an appropriate matched filter can provide improved performance.

}, keywords = {Algorithms, Cerebral Cortex, Cortical Synchronization, Electroencephalography, Evoked Potentials, Humans, Imagination, Pattern Recognition, Automated, User-Computer Interface}, issn = {0018-9294}, doi = {10.1109/TBME.2006.886661}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17278584}, author = {Krusienski, Dean J and Gerwin Schalk and Dennis J. McFarland and Jonathan Wolpaw} }