Florian Losch

The Berlin brain-computer interface: EEG-based communication without subject training

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 similarly 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.

Combined Optimization of Spatial and Temporal Filters for Improving Brain-Computer Interfacing

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: Accurate Performance From First-Session in BCI-NaÏve Subjects

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.

Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis

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 subjects 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.

Therapeutic Vaccination With Recombinant Adenovirus Reduces Splenic Parasite Burden in Experimental Visceral Leishmaniasis

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.

Improving speed and accuracy of brain-computer interfaces using readiness potential features

To enhance human interaction with machines, research interest is growing to develop a brain-computer interface, which allows communication of a human with a machine only by use of brain signals. So far, the applicability of such an interface is strongly limited by low bit-transfer rates, slow response times and long training sessions for the subject. The Berlin Brain-Computer Interface (BBCI) project is guided by the idea to train a computer by advanced machine learning techniques both to improve classification performance and to reduce the need of subject training. In this paper we present two directions in which brain-computer interfacing can be enhanced by exploiting the lateralized readiness potential: (1) for establishing a rapid response BCI system that can predict the laterality of upcoming finger movements before EMG onset even in time critical contexts, and (2) to improve information transfer rates in the common BCI approach relying on imagined limb movements.

Differential recruitment of high frequency wavelets (600 Hz) and primary cortical response (N20) in human median nerve somatosensory evoked potentials

Human median nerve somatosensory evoked potentials contain a burst of high-frequency (600 Hz) wavelets superimposed on the primary cortical response (N20). These presumably reflect highly-synchronized repetitive thalamic and/or intracortical population spike bursts and are diminished in non-REM sleep with N20 persisting. Here the burst/N20 relation in awake subjects was examined by using eight different intensities of electric median nerve stimuli. In all subjects the amplitude recruitment of both N20 and burst could be modeled adequately as a sigmoidal function of stimulus intensity. While 8/10 subjects showed a parallel recruitment, 2/10 subjects required significantly higher stimulation intensities for burst than for N20 recruitment. This dampened burst recruitment possibly reflects slight vigilance fluctuations in open-eyed awake subjects; a further increase of burst thresholds could explain the burst attenuation when entering shallow sleep.

Identification of Noncanonical Melanoma-Associated T Cell Epitopes for Cancer Immunotherapy

The identification of tumor-associated T cell epitopes has contributed significantly to the understanding of the interrelationship of tumor and immune system and is instrumental in the development of therapeutic vaccines for the treatment of cancer. Most of the known epitopes have been identified with prediction algorithms that compute the potential capacity of a peptide to bind to HLA class I molecules. However, naturally expressed T cell epitopes need not necessarily be strong HLA binders. To overcome this limitation of the available prediction algorithms we established a strategy for the identification of T cell epitopes that include suboptimal HLA binders. To this end, an artificial neural network was developed that predicts HLA-binding peptides in protein sequences by taking the entire sequence context into consideration rather than computing the sum of the contribution of the individual amino acids. Using this algorithm, we predicted seven HLA A*0201-restricted potential T cell epitopes from known melanoma-associated Ags that do not conform to the canonical anchor motif for this HLA molecule. All seven epitopes were validated as T cell epitopes and three as naturally processed by melanoma tumor cells. T cells for four of the new epitopes were found at elevated frequencies in the peripheral blood of melanoma patients. Modification of the peptides to the canonical sequence motifs led to improved HLA binding and to improved capacity to stimulate T cells.

Specific adverse effects of antiepileptic drugs - a true-to-life monotherapy study

BACKGROUNDnIn patients taking antiepileptic drugs (AEDs) for epilepsy, adverse effects (AEs) often lead to unfavorable quality of life, impaired adherence, and, eventually, discontinuation of pharmacological treatment. In a true-to-life sample of subjects from our academic epilepsy outpatient clinic, we aimed to identify predictors for overall high AE burden and for specific AEs focusing on patients on monotherapy.nnnMETHODSnAll patients ≥16years of age with epilepsy for ≥12months were routinely asked to complete the Liverpool Adverse Event Profile (LAEP) just before their appointment. Demographic, epilepsy, and treatment variables were derived from our comprehensive outpatient database.nnnRESULTSnOut of 841 patients, 438 (61% female, mean age: 44.7±17.1years) on monotherapy were included in this study. Levetiracetam (n=151), lamotrigine (n=167), valproic acid (n=73), or controlled-release carbamazepine (n=47) were the most commonly used antiepileptic drugs (AEDs). Independent predictors for general high AE burden (LAEP score≥45) were duration of epilepsy, lack of 12-month seizure freedom, and partial epilepsy, but none of the four individual AEDs. The most frequent LAEP-defined specific AEs were sleepiness, difficulty concentrating, tiredness, and memory problems. The three most frequent independent predictors for each of the 19 AEs were lack of 12-month seizure freedom (13/19 AEs), individual AED (7/19 AEs), and partial epilepsy (6/19 AEs). Levetiracetam was independently associated with anger/aggression, nervousness/agitation, upset stomach, depression, and sleep disturbance; lamotrigine with nervousness/agitation, upset stomach, and difficulty concentrating; and valproic acid with upset stomach and shaky hands.nnnCONCLUSIONnIndividual AEDs independently predicted some specific AEs, but not overall high AE burden. Our findings may help to characterize patients with epilepsy who are at high risk for specific AEs. Dose reduction or change to another AED may reduce LAEP score and potential nonadherence.

Therapeutic vaccination for cancer immunotherapy: Antigen selection and clinical responses

Background: Because of its high specificity and low toxicity therapeutic vaccination is considered a desirable treatment for cancer. So far, however, the results of cancer vaccination trials have been disappointing, which is often attributed to the problem identifying appropriate vaccine antigens. Tumorassociated antigens are mostly autoantigens and therefore expected to be subject to immunosuppressive mechanisms. Cancer-testis antigens are the most prominent exception as, still being self, they are physiologically only expressed in immunopriviledged tissues and should therefore not induce autotolerance. This leads to the widely accepted hypothesis that cancer-testis antigens should be more efficient inducers of anti-tumor cellular immune responses than differentiation antigens. Aim of the study was to test this hypothesis by evaluating the published reports on clinical therapeutic vaccination trials for the objective clinical response rates to vaccination with cancer testis antigen vs. differentiation antigens. Approach: The results of vaccination clinical trials with cancer testis and/or differentiation antigens published in literature and databanks were analyzed for clinical outcome versus vaccine antigens. 21 publications on cancer testis antigen-based trials in which clinical outcome was reported according to WHO or RECIST were identified and analyzed. Results: The rate of objective responses to cancer testis antigen vaccines in 239 patients was 3.8% and for the 235 patients vaccinated with cancer testis plus 3 differentiation antigens 4.3% compared to 2.6% for the 496 patients vaccinated with differentiation antigens alone. Conclusions: Cancer testis antigen-based vaccines seem slightly superior over vaccines based on differentiation antigens providing support for the hypothesis.