@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 {2186, title = {Two-dimensional movement control using electrocorticographic signals in humans.}, journal = {J Neural Eng}, volume = {5}, year = {2008}, month = {03/2008}, pages = {75-84}, abstract = {

We show here that a\ brain-computer\ interface (BCI) using electrocorticographic activity (ECoG) and imagined or overt motor tasks enables humans to control a\ computer\ cursor in two dimensions. Over a brief training period of 12-36 min, each of five human subjects acquired substantial control of particular ECoG features recorded from several locations over the same hemisphere, and achieved average success rates of 53-73\% in a two-dimensional four-target center-out task in which chance accuracy was 25\%. Our results support the expectation that ECoG-based BCIs can combine high performance with technical and\ clinical\ practicality, and also indicate promising directions for further research.

}, keywords = {Adolescent, Adult, Brain Mapping, Data Interpretation, Statistical, Drug Resistance, Electrocardiography, Electrodes, Implanted, Electroencephalography, Epilepsy, Female, Humans, Male, Movement, User-Computer Interface}, issn = {1741-2560}, doi = {10.1088/1741-2560/5/1/008}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18310813}, author = {Gerwin Schalk and Miller, K.J. and Nicholas R Anderson and Adam J Wilson and Smyth, Matt and Ojemann, J G and Moran, D and Jonathan Wolpaw and Leuthardt, E C} }