@article {2183, title = {Brain-computer interfaces (BCIs): Detection Instead of Classification.}, journal = {J Neurosci Methods}, volume = {167}, year = {2008}, month = {01/2008}, pages = {51-62}, abstract = {

Many studies over the past two decades have shown that people can use\ brain\ signals to convey their intent to a\ computer\ through\ brain-computer interfaces\ (BCIs). These devices operate by recording signals from the\ brain\ and translating these signals into device commands. They can be used by people who are severely paralyzed to communicate without any use of muscle activity. One of the major impediments in translating this novel technology into\ clinical\ applications is the current requirement for preliminary analyses to identify the\ brain\ signal features best suited for communication. This paper introduces and validates signal detection, which does not require such analysis procedures, as a new concept in BCI signal processing. This detection concept is realized with Gaussian mixture models (GMMs) that are used to model resting\ brain\ activity so that any change in\ relevantbrain\ signals can be detected. It is implemented in a package called SIGFRIED (SIGnal modeling For Real-time Identification and Event Detection). The results indicate that SIGFRIED produces results that are within the range of those achieved using a common analysis strategy that requires preliminary identification of signal features. They indicate that such laborious analysis procedures could be replaced by merely recording\ brain\ signals during rest. In summary, this paper demonstrates how SIGFRIED could be used to overcome one of the present impediments to translation of laboratory BCI demonstrations into clinically practical applications.

}, keywords = {Adult, Algorithms, Brain, Brain Mapping, Electrocardiography, Electroencephalography, Humans, Male, Man-Machine Systems, Normal Distribution, Online Systems, Signal Detection, Psychological, Signal Processing, Computer-Assisted, Software Validation, User-Computer Interface}, issn = {0165-0270}, doi = {10.1016/j.jneumeth.2007.08.010}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17920134}, author = {Gerwin Schalk and Peter Brunner and Lester A Gerhardt and H Bischof and Jonathan Wolpaw} } @article {2172, title = {The BCI competition III: Validating alternative approaches to actual BCI problems.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {14}, year = {2006}, month = {06/2006}, pages = {153-9}, abstract = {

A\ brain-computer interface\ (BCI) is a system that allows its users to control external devices with\ brainactivity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user{\textquoteright}s\ brain, which produces\ brain\ activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual\ online\ use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.

}, keywords = {Algorithms, Brain, Communication Aids for Disabled, Databases, Factual, Electroencephalography, Evoked Potentials, Humans, Neuromuscular Diseases, Software Validation, Technology Assessment, Biomedical, User-Computer Interface}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875642}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792282}, author = {Benjamin Blankertz and M{\"u}ller, Klaus-Robert and Krusienski, Dean J and Gerwin Schalk and Jonathan Wolpaw and Schl{\"o}gl, Alois and Pfurtscheller, Gert and Mill{\'a}n, Jos{\'e} del R and Schr{\"o}der, Michael and Niels Birbaumer} } @article {2115, title = {Particle picking by segmentation: a comparative study with SPIDER-based manual particle picking.}, journal = {J Struct Biol}, volume = {152}, year = {2005}, month = {12/2005}, pages = {211-20}, abstract = {

Boxing hundreds of thousands of particles in low-dose electron micrographs is one of the major bottle-necks in advancing toward achieving atomic resolution reconstructions of biological macromolecules. We have shown that a combination of pre-processing operations and segmentation can be used as an effective, automatic tool for identifying and boxing single-particle images. This paper provides a brief description of how this method has been applied to a large data set of micrographs of ice-embedded ribosomes, including a comparative analysis of the efficiency of the method. Some results on processing micrographs of tripeptidyl peptidase II particles are also shown. In both cases, we have achieved our goal of selecting at least 80\% of the particles that an expert would select with less than 10\% false positives.

}, keywords = {Algorithms, Aminopeptidases, Cryoelectron Microscopy, Dipeptidyl-Peptidases and Tripeptidyl-Peptidases, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, Internet, Particle Size, Ribosomes, Serine Endopeptidases, Software, Software Validation}, issn = {1047-8477}, doi = {10.1016/j.jsb.2005.09.007}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16330229}, author = {Adiga, Umesh and Baxter, Bill and Hall, Richard J and Rockel, Beate and Rath, Bimal K and Frank, Joachim and Glaeser, Robert M} }