Review of the BCI Competition IV. - Tangermann, M.; Muller, K.R.; Aertsen, A.; Niels Birbaumer; Christoph Braun; Brunner, Clemens; Leeb, R.; Mehring, C.; Miller, K.J.; Mueller-Putz, G.; Nolte, G.; Pfurtscheller, G.; et al. (Frontiers in Neuroprosthetics, 2012)
The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students. The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) session-to-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.
Keywords: BCI; brain-computer interface; competition

An auditory brain-computer interface (BCI). - Nijboer, Femke; Adrian Furdea; Gunst, Ingo; Mellinger, Jürgen; Dennis J. McFarland; Niels Birbaumer; Kübler, Andrea (Journal of neuroscience methods, 2008)
Brain-computer interfaces (BCIs) translate brain activity into signals controlling external devices. BCIs based on visual stimuli can maintain communication in severely paralyzed patients, but only if intact vision is available. Debilitating neurological disorders however, may lead to loss of intact vision. The current study explores the feasibility of an auditory BCI. Sixteen healthy volunteers participated in three training sessions consisting of 30 2-3 min runs in which they learned to increase or decrease the amplitude of sensorimotor rhythms (SMR) of the EEG. Half of the participants were presented with visual and half with auditory feedback. Mood and motivation were assessed prior to each session. Although BCI performance in the visual feedback group was superior to the auditory feedback group there was no difference in performance at the end of the third session. Participants in the auditory feedback group learned slower, but four out of eight reached an accuracy of over 70% correct in the last session comparable to the visual feedback group. Decreasing performance of some participants in the visual feedback group is related to mood and motivation. We conclude that with sufficient training time an auditory BCI may be as efficient as a visual BCI. Mood and motivation play a role in learning to use a BCI.
Keywords: auditory feedback; brain-computer interface; EEG; locked-in state; motivation; sensorimotor rhythm

A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. - Nijboer, F.; Sellers, E. W.; Mellinger, J.; Jordan, M. A.; Matuz, T.; Adrian Furdea; S Halder; Mochty, U.; Krusienski, D. J.; Theresa M Vaughan; Jonathan Wolpaw; Niels Birbaumer; et al. (Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2008)
OBJECTIVE: The current study evaluates the efficacy of a P300-based brain-computer interface (BCI) communication device for individuals with advanced ALS. METHODS: Participants attended to one cell of a N x N matrix while the N rows and N columns flashed randomly. Each cell of the matrix contained one character. Every flash of an attended character served as a rare event in an oddball sequence and elicited a P300 response. Classification coefficients derived using a stepwise linear discriminant function were applied to the data after each set of flashes. The character receiving the highest discriminant score was presented as feedback. RESULTS: In Phase I, six participants used a 6 x 6 matrix on 12 separate days with a mean rate of 1.2 selections/min and mean online and offline accuracies of 62% and 82%, respectively. In Phase II, four participants used either a 6 x 6 or a 7 x 7 matrix to produce novel and spontaneous statements with a mean online rate of 2.1 selections/min and online accuracy of 79%. The amplitude and latency of the P300 remained stable over 40 weeks. CONCLUSIONS: Participants could communicate with the P300-based BCI and performance was stable over many months. SIGNIFICANCE: BCIs could provide an alternative communication and control technology in the daily lives of people severely disabled by ALS.
Keywords: Amyotrophic Lateral Sclerosis; brain-computer interface; electroencephalogram; event-related potentials; P300; Rehabilitation

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