TY - JOUR T1 - A scanning protocol for a sensorimotor rhythm-based brain-computer interface. JF - Biological psychology Y1 - 2009 A1 - Friedrich, Elisabeth V. C. A1 - Dennis J. McFarland A1 - Neuper, Christa A1 - Theresa M Vaughan A1 - Peter Brunner A1 - Jonathan Wolpaw KW - BCI KW - brain-computer interface KW - scanning protocol KW - sensorimotor rhythm AB - The scanning protocol is a novel brain-computer interface (BCI) implementation that can be controlled with sensorimotor rhythms (SMRs) of the electroencephalogram (EEG). The user views a screen that shows four choices in a linear array with one marked as target. The four choices are successively highlighted for 2.5s each. When a target is highlighted, the user can select it by modulating the SMR. An advantage of this method is the capacity to choose among multiple choices with just one learned SMR modulation. Each of 10 naive users trained for ten 30 min sessions over 5 weeks. User performance improved significantly (p<0.001) over the sessions and ranged from 30 to 80% mean accuracy of the last three sessions (chance accuracy=25%). The incidence of correct selections depended on the target position. These results suggest that, with further improvements, a scanning protocol can be effective. The ultimate goal is to expand it to a large matrix of selections. VL - 80 UR - http://www.ncbi.nlm.nih.gov/pubmed/18786603 ER - TY - JOUR T1 - The BCI Competition 2003: Progress and perspectives in detection and discrimination of EEG single trials. JF - IEEE Trans Biomed Eng Y1 - 2004 A1 - Benjamin Blankertz A1 - Müller, Klaus-Robert A1 - Curio, Gabriel A1 - Theresa M Vaughan A1 - Gerwin Schalk A1 - Jonathan Wolpaw A1 - Schlögl, Alois A1 - Neuper, Christa A1 - Pfurtscheller, Gert A1 - Hinterberger, T. A1 - Schröder, Michael A1 - Niels Birbaumer KW - Adult KW - Algorithms KW - Amyotrophic Lateral Sclerosis KW - Artificial Intelligence KW - Brain KW - Cognition KW - Databases, Factual KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Reproducibility of Results KW - Sensitivity and Specificity KW - User-Computer Interface AB - Interest in developing a new method of man-to-machine communication--a brain-computer interface (BCI)--has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms. VL - 51 IS - 6 ER - TY - JOUR T1 - The BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials. JF - IEEE transactions on bio-medical engineering Y1 - 2004 A1 - Benjamin Blankertz A1 - Müller, Klaus-Robert A1 - Curio, Gabriel A1 - Theresa M Vaughan A1 - Gerwin Schalk A1 - Jonathan Wolpaw A1 - Schlögl, Alois A1 - Neuper, Christa A1 - Pfurtscheller, Gert A1 - Hinterberger, Thilo A1 - Schröder, Michael A1 - Niels Birbaumer KW - augmentative communication KW - BCI KW - beta-rhythm KW - brain-computer interface KW - EEG KW - ERP KW - imagined hand movements KW - lateralized readiness potential KW - mu-rhythm KW - P300 KW - Rehabilitation KW - single-trial classification KW - slow cortical potentials AB - Interest in developing a new method of man-to-machine communication–a brain-computer interface (BCI)–has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms. VL - 51 UR - http://www.ncbi.nlm.nih.gov/pubmed/15188876 ER -