Robust EEG Channel Selection across Subjects for Brain-Computer Interfaces. - Schröder, Michael; Lal, T.N; Hinterberger, T.; Bogdan, Martin; Jeremy Jeremy Hill; Niels Birbaumer; Rosenstiel, W.; Schölkopf, B (EURASIP Journal on Advances in Signal Processing, 2005)
Most EEG-based brain-computer interface (BCI) paradigms come along with specific electrode positions, for example, for a visual-based BCI, electrode positions close to the primary visual cortex are used. For new BCI paradigms it is usually not known where task relevant activity can be measured from the scalp. For individual subjects, Lal et al. in 2004 showed that recording positions can be found without the use of prior knowledge about the paradigm used. However it remains unclear to what extent their method of recursive channel elimination (RCE) can be generalized across subjects. In this paper we transfer channel rankings from a group of subjects to a new subject. For motor imagery tasks the results are promising, although cross-subject channel selection does not quite achieve the performance of channel selection on data of single subjects. Although the RCE method was not provided with prior knowledge about the mental task, channels that are well known to be important (from a physiological point of view) were consistently selected whereas task-irrelevant channels were reliably disregarded.
Keywords: brain-computer interface; channel selection; Electroencephalography; feature selection; recursive channel elimination; support vector machine

The BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials. - Benjamin Blankertz; Müller, Klaus-Robert; Curio, Gabriel; Theresa M Vaughan; Gerwin Schalk; Jonathan Wolpaw; Schlögl, Alois; Neuper, Christa; Pfurtscheller, Gert; Hinterberger, Thilo; Schröder, Michael; Niels Birbaumer (IEEE transactions on bio-medical engineering, 2004)
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.
Keywords: augmentative communication; BCI; beta-rhythm; brain-computer interface; EEG; ERP; imagined hand movements; lateralized readiness potential; mu-rhythm; P300; Rehabilitation; single-trial classification; slow cortical potentials