@article{DornhegeBlankertzCurioetal.2004, author = {Dornhege, Guido and Blankertz, Benjamin and Curio, Gabriel and M{\"u}ller, Klaus-Robert}, title = {Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms}, year = {2004}, abstract = {Noninvasive electroencephalogram (EEG) recordings provide for easy and safe access to human neocortical processes which can be exploited for a brain-computer interface (BCI). At present, however, the use of BCIs is severely limited by low bit-transfer rates. We systematically analyze and develop two recent concepts, both capable of enhancing the information gain from multichannel scalp EEG recordings: 1) the combination of classifiers, each specifically tailored for different physiological phenomena, e.g., slow cortical potential shifts, such as the premovement Bereitschaftspotential or differences in spatio-spectral distributions of brain activity (i.e., focal event-related desynchronizations) and 2) behavioral paradigms inducing the subjects to generate one out of several brain states (multiclass approach) which all bare a distinctive spatio-temporal signature well discriminable in the standard scalp EEG. We derive information-theoretic predictions and demonstrate their relevance in experimental data. We will show that a suitably arranged interaction between these concepts can significantly boost BCI performances}, language = {en} } @article{BlankertzDornhegeKrauledatetal.2006, author = {Blankertz, Benjamin and Dornhege, Guido and Krauledat, Matthias and M{\"u}ller, Klaus-Robert and Kunzmann, Volker and Losch, Florian and Curio, Gabriel}, title = {The Berlin brain-computer interface : EEG-based communication without subject training}, issn = {1534-4320}, doi = {10.1109/Tnsre.2006.875557}, year = {2006}, abstract = {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}, language = {en} } @phdthesis{Dornhege2006, author = {Dornhege, Guido}, title = {Increasing information transfer rates for brain-computer interfacing}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-7690}, school = {Universit{\"a}t Potsdam}, year = {2006}, abstract = {The goal of a Brain-Computer Interface (BCI) consists of the development of a unidirectional interface between a human and a computer to allow control of a device only via brain signals. While the BCI systems of almost all other groups require the user to be trained over several weeks or even months, the group of Prof. Dr. Klaus-Robert M{\"u}ller in Berlin and Potsdam, which I belong to, was one of the first research groups in this field which used machine learning techniques on a large scale. The adaptivity of the processing system to the individual brain patterns of the subject confers huge advantages for the user. Thus BCI research is considered a hot topic in machine learning and computer science. It requires interdisciplinary cooperation between disparate fields such as neuroscience, since only by combining machine learning and signal processing techniques based on neurophysiological knowledge will the largest progress be made. In this work I particularly deal with my part of this project, which lies mainly in the area of computer science. I have considered the following three main points: Establishing a performance measure based on information theory: I have critically illuminated the assumptions of Shannon's information transfer rate for application in a BCI context. By establishing suitable coding strategies I was able to show that this theoretical measure approximates quite well to what is practically achieveable. Transfer and development of suitable signal processing and machine learning techniques: One substantial component of my work was to develop several machine learning and signal processing algorithms to improve the efficiency of a BCI. Based on the neurophysiological knowledge that several independent EEG features can be observed for some mental states, I have developed a method for combining different and maybe independent features which improved performance. In some cases the performance of the combination algorithm outperforms the best single performance by more than 50 \%. Furthermore, I have theoretically and practically addressed via the development of suitable algorithms the question of the optimal number of classes which should be used for a BCI. It transpired that with BCI performances reported so far, three or four different mental states are optimal. For another extension I have combined ideas from signal processing with those of machine learning since a high gain can be achieved if the temporal filtering, i.e., the choice of frequency bands, is automatically adapted to each subject individually. Implementation of the Berlin brain computer interface and realization of suitable experiments: Finally a further substantial component of my work was to realize an online BCI system which includes the developed methods, but is also flexible enough to allow the simple realization of new algorithms and ideas. So far, bitrates of up to 40 bits per minute have been achieved with this system by absolutely untrained users which, compared to results of other groups, is highly successful.}, subject = {Kybernetik}, language = {en} } @article{HarmelingDornhegeTaxetal.2006, author = {Harmeling, Stefan and Dornhege, Guido and Tax, David and Meinecke, Frank C. and M{\"u}ller, Klaus-Robert}, title = {From outliers to prototypes : Ordering data}, issn = {0925-2312}, doi = {10.1016/j.neucom.2005.05.015}, year = {2006}, abstract = {We propose simple and fast methods based on nearest neighbors that order objects from high-dimensional data sets from typical points to untypical points. On the one hand, we show that these easy-to-compute orderings allow us to detect outliers (i.e. very untypical points) with a performance comparable to or better than other often much more sophisticated methods. On the other hand, we show how to use these orderings to detect prototypes (very typical points) which facilitate exploratory data analysis algorithms such as noisy nonlinear dimensionality reduction and clustering. Comprehensive experiments demonstrate the validity of our approach.}, language = {en} } @article{DornhegeBlankertzKrauledatetal.2006, author = {Dornhege, Guido and Blankertz, Benjamin and Krauledat, Matthias and Losch, Florian and Curio, Gabriel and M{\"u}ller, Klaus-Robert}, title = {Combined optimization of spatial and temporal filters for improving brain-computer interfacing}, series = {IEEE transactions on bio-medical electronics}, volume = {53}, journal = {IEEE transactions on bio-medical electronics}, number = {11}, publisher = {IEEE}, address = {New York}, issn = {0018-9294}, doi = {10.1109/TBME.2006.883649}, pages = {2274 -- 2281}, year = {2006}, abstract = {Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output de ice 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.}, language = {en} }