Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device by bypassing conventional motor output pathways of nerves and muscles. Therefore they could provide a new communication and control option for paralyzed patients. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. Here we present a novel technique that allows the simultaneous optimization of a spatial and a spectral filter enhancing discriminability rates of multichannel EEG single-trials. The evaluation of 60 experiments involving 22 different subjects demonstrates the significant superiority of the proposed algorithm over to its classical counterpart: the median classification error rate was decreased by 11%. Apart from the enhanced classification, the spatial and/or the spectral filter that are determined by the algorithm can also be used for further analysis of the data, e.g., for source localization of the respective brain rhythms.

The Berlin Brain-Computer Interface (BBCI) project develops a noninvasive BCI system whose key features are 1) the use of well-established motor competences as control paradigms, 2) high-dimensional features from 128-channel electroencephalogram (EEG), and 3) advanced machine learning techniques. As reported earlier, our experiments demonstrate that very high information transfer rates can be achieved using the readiness potential (RP) when predicting the laterality of upcoming left- versus right-hand movements in healthy subjects. A more recent study showed that the RP similarily accompanies phantom movements in arm amputees, but the signal strength decreases with longer loss of the limb. In a complementary approach, oscillatory features are used to discriminate imagined movements (left hand versus right hand versus foot). In a recent feedback study with six healthy subjects with no or very little experience with BCI control, three subjects achieved an information transfer rate above 35 bits per minute (bpm), and further two subjects above 24 and 15 bpm, while one subject could not achieve any BCI control. These results are encouraging for an EEG-based BCI system in untrained subjects that is independent of peripheral nervous system activity and does not rely on evoked potentials even when compared to results with very well-trained subjects operating other BCI systems.

The Berlin Brain--Computer Interface (BBCI) project develops a noninvasive BCI system whose key features are: 1) the use of well-established motor competences as control paradigms; 2) high-dimensional features from multichannel EEG; and 3) advanced machine-learning techniques. Spatio-spectral changes of sensorimotor rhythms are used to discriminate imagined movements (left hand, right hand, and foot). A previous feedback study [M. Krauledat, K.-R. MUller, and G. Curio. (2007) The non-invasive Berlin brain--computer Interface: Fast acquisition of effective performance in untrained subjects. NeuroImage. [Online]. 37(2), pp. 539--550. Available: http://dx.doi.org/10.1016/j.neuroimage.2007.01.051] with ten subjects provided preliminary evidence that the BBCI system can be operated at high accuracy for subjects with less than five prior BCI exposures. Here, we demonstrate in a group of 14 fully BCI-naIve subjects that 8 out of 14 BCI novices can perform at >84% accuracy in their very first BCI session, and a further four subjects at >70%. Thus, 12 out of 14 BCI-novices had significant above-chance level performances without any subject training even in the first session, as based on an optimized EEG analysis by advanced machine-learning algorithms.

Brain-computer interfaces (BCIs) involve two coupled adapting systems--the human subject and the computer. In developing our BCI, our goal was to minimize the need for subject training and to impose the major learning load on the computer. To this end, we use behavioral paradigms that exploit single-trial EEG potentials preceding voluntary finger movements. Here, we report recent results on the basic physiology of such premovement event-related potentials (ERP). 1) We predict the laterality of imminent left- versus right-hand finger movements in a natural keyboard typing condition and demonstrate that a single-trial classification based on the lateralized Bereitschaftspotential (BP) achieves good accuracies even at a pace as fast as 2 taps/s. Results for four out of eight subjects reached a peak information transfer rate of more than 15 b/min; the four other subjects reached 6-10 b/min. 2) We detect cerebral error potentials from single false-response trials in a forced-choice task, reflecting the subject's recognition of an erroneous response. Based on a specifically tailored classification procedure that limits the rate of false positives at, e.g., 2%, the algorithm manages to detect 85% of error trials in seven out of eight subjects. Thus, concatenating a primary single-trial BP-paradigm involving finger classification feedback with such secondary error detection could serve as an efficient online confirmation/correction tool for improvement of bit rates in a future BCI setting. As the present variant of the Berlin BCI is designed to achieve fast classifications in normally behaving subjects, it opens a new perspective for assistance of action control in time-critical behavioral contexts; the potential transfer to paralyzed patients will require further study.

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Therapeutic vaccines, when used alone or in combination therapy with antileishmanial drugs, may have an important place in the control of a variety of forms of human leishmaniasis. Here, we describe the development of an adenovirus-based vaccine (Ad5-KH) comprising a synthetic haspb gene linked to a kmp11 gene via a viral 2A sequence. In nonvaccinated Leishmania donovani–infected BALB/c mice, HASPB- and KMP11-specific CD8+ T cell responses were undetectable, although IgG1 and IgG2a antibodies were evident. After therapeutic vaccination, antibody responses were boosted, and IFNγ+CD8+ T cell responses, particularly to HASPB, became apparent. A single vaccination with Ad5-KH inhibited splenic parasite growth by ∼66%, a level of efficacy comparable to that observed in early stage testing of clinically approved antileishmanial drugs in this model. These studies indicate the usefulness of adenoviral vectors to deliver leishmanial antigens in a potent and host protective manner to animals with existing L. donovani infection.

The leishmaniases are protozoal diseases that severely affect large populations in tropical and subtropical regions. There are only limited treatment options and preventative measures. Vaccines will be important for prevention, control and elimination of leishmaniasis, and could reduce the transmission and burden of disease in endemic populations. We report the development of a DNA vaccine against leishmaniasis that induced T cell-based immunity and is a candidate for clinical trials. The vaccine antigens were selected as conserved in various Leishmania species, different endemic regions, and over time. They were tested with T cells from individuals cured of leishmaniasis, and shown to be immunogenic and to induce CD4(+) and CD8(+) T cell responses in genetically diverse human populations of different endemic regions. The vaccine proved protective in a rodent model of infection. Thus, the immunogenicity of candidate vaccine antigens in human populations of endemic regions, as well as proof of principle for induction of specific immune responses and protection against Leishmania infection in mice, provides a viable strategy for T cell vaccine development.

A solid phase-based strategy gave access to DNA-tagged heterocycles by metal-mediated imine chemistry, exemplified by Cushman- and 1,3-dipolar cycloaddition reactions.