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Adler J.,Friedrich Wilhelm Bessel Institute | Orlik B.,University of Bremen
2015 IEEE 6th International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2015 | Year: 2015

Power generation using renewable resources is replacing conventional generation using steam power plants all over the world. Therefore a new control concept is shown, enabling a wind power station to behave just as like the synchronous generator in a steam power plant providing ancillary services. The main focus is on the provision of instantaneous and primary reserve and the ability to dampen grid power oscillations. Therefore only changes in the control of the inverter of the wind power station need to be performed. The results of detailed simulations are presented and an experimental test stand is shown. © 2015 IEEE.


Volosyak I.,University of Bremen | Valbuena D.,Friedrich Wilhelm Bessel Institute | Malechka T.,University of Bremen | Malechka T.,Friedrich Wilhelm Bessel Institute | And 3 more authors.
Journal of Neural Engineering | Year: 2010

Current brain-computer interfaces (BCIs) that make use of EEG acquisition techniques require unpleasant electrode gel causing skin abrasion during the standard preparation procedure. Electrodes that require tap water instead of electrolytic electrode gel would make both daily setup and clean up much faster, easier and comfortable. This paper presents the results from ten subjects that controlled an SSVEP-based BCI speller system using two EEG sensor modalities: water-based and gel-based surface electrodes. Subjects performed in copy spelling mode using conventional gel-based electrodes and water-based electrodes with a mean information transfer rate (ITR) of 29.68 ± 14.088 bit min -1 and of 26.56 ± 9.224 bit min -1, respectively. A paired t-test failed to reveal significant differences in the information transfer rates and accuracies of using gel- or water-based electrodes for EEG acquisition. This promising result confirms the operational readiness of water-based electrodes for BCI applications. © 2010 IOP Publishing Ltd.


Valbuena D.,Friedrich Wilhelm Bessel Institute | Volosyak I.,University of Bremen | Graser A.,University of Bremen
2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 | Year: 2010

Brain-computer interface (BCI) systems enable communication and control without movement. Although advanced signal processing methods are used in BCI research, the output of a BCI is still unreliable, and the information transfer rates are very low compared with conventional human interaction interfaces such as keyboard and mouse. Therefore, improvements in signal classification methods and the exploitation of the learning skills of the user are required to compensate the unreliability of the BCI system. This work analyzes the response time of the Bremen-BCI based on steady-state visual evoked potentials (SSVEP) previously tested on 27 subjects, and presents an enhanced method for faster detection of SSVEP responses. The aim is toward the development of a swift BCI (sBCI) that robustly detects the exact time point where the user starts modulating his brain signals. © 2010 IEEE.


Volosyak I.,University of Bremen | Valbuena D.,Friedrich Wilhelm Bessel Institute | Luth T.,University of Bremen | Malechka T.,University of Bremen | Graser A.,University of Bremen
IEEE Transactions on Neural Systems and Rehabilitation Engineering | Year: 2011

Braincomputer interface (BCI) systems use brain activity as an input signal and enable communication without movement. This study is a successor of our previous study (BCI demographics I) and examines correlations among BCI performance, personal preferences, and different subject factors such as age or gender for two sets of steady-state visual evoked potential (SSVEP) stimuli: one in the medium frequency range (13, 14, 15 and 16 Hz) and another in the high-frequency range (34, 36, 38, 40 Hz). High-frequency SSVEPs (above 30 Hz) diminish user fatigue and risk of photosensitive epileptic seizures. Results showed that most people, despite having no prior BCI experience, could use the SSVEP-based Bremen-BCI system in a very noisy field setting at a fair. Results showed that demographic parameters as well as handedness, tiredness, alcohol and caffeine consumption, etc., have no significant effect on the performance of SSVEP-based BCI. Most subjects did not consider the flickering stimuli annoying, only five out of total 86 participants indicated change in fatigue during the experiment. 84 subjects performed with a mean information transfer rate of 17.24 ± 6.99 bit/min and an accuracy of 92.26 ± 7.82% with the medium frequency set, whereas only 56 subjects performed with a mean information transfer rate of 12.10 ± 7.31 bit/min and accuracy of 89.16 ± 9.29% with the high-frequency set. These and other demographic analyses may help identify the best BCI for each user. © 2011 IEEE.


Hillers B.,Friedrich Wilhelm Bessel Institute | Gui V.,Polytechnic University of Timişoara | Graeser A.,University of Bremen
2012 10th International Symposium on Electronics and Telecommunications, ISETC 2012 - Conference Proceedings | Year: 2012

This paper describes a new method for enhancing the local contrast of high dynamical range images on conventional low dynamical range displays. We use the mean shift clustering algorithm to segment the image and enhance segments using contrast limited adaptive histogram equalization (CLAHE) in combination with a new kernel based interpolation technique. Our main application is the enhancement of welding image sequences, but we tested our method on a larger image database. Experiments demonstrate improvements over the traditional CLAHE based image enhancement. © 2012 IEEE.


Borecki J.,University of Bremen | Groke H.,University of Bremen | Orlik B.,University of Bremen | Joost M.,Friedrich Wilhelm Bessel Institute
Joint International Conference - ACEMP 2015: Aegean Conference on Electrical Machines and Power Electronics, OPTIM 2015: Optimization of Electrical and Electronic Equipment and ELECTROMOTION 2015: International Symposium on Advanced Electromechanical Motion Systems | Year: 2015

The following publication describes the method used to calculate current waveforms optimized for minimum RMS value of the current and at the same time ripple-free output torque of the transverse flux reluctance machine. Due to the similarities with switched reluctance machines it is also applicable for this type of electric motors. The method described here, requires detailed characteristics of the machine. This algorithm is based on calculating a range of current combinations for every position instance that will result with set torque value, and then choosing the optimal path through all the position instants with help of dynamic programming. Results of the calculations for an example machine are presented and discussed. In the conclusions section some advantages and disadvantages of this algorithm as well as possible improvements, are briefly discussed. © 2015 IEEE.


Kus R.,University of Warsaw | Zygierewicz J.,University of Warsaw | Malechka T.,University of Bremen | Malechka T.,Friedrich Wilhelm Bessel Institute | And 2 more authors.
IEEE Transactions on Neural Systems and Rehabilitation Engineering | Year: 2012

A new multiclass brain-computer interface (BCI) based on the modulation of sensorimotor oscillations by imagining movements is described. By the application of advanced signal processing tools, statistics and machine learning, this BCI system offers: 1) asynchronous mode of operation, 2) automatic selection of user-dependent parameters based on an initial calibration, 3) incremental update of the classifier parameters from feedback data. The signal classification uses spatially filtered signals and is based on spectral power estimation computed in individualized frequency bands, which are automatically identified by a specially tailored AR-based model. Relevant features are chosen by a criterion based on Mutual Information. Final recognition of motor imagery is effectuated by a multinomial logistic regression classifier. This BCI system was evaluated in two studies. In the first study, five participants trained the ability to imagine movements of the right hand, left hand and feet in response to visual cues. The accuracy of the classifier was evaluated across four training sessions with feedback. The second study assessed the information transfer rate (ITR) of the BCI in an asynchronous application. The subjects' task was to navigate a cursor along a computer rendered 2-D maze. A peak information transfer rate of 8.0 bit/min was achieved. Five subjects performed with a mean ITR of 4.5 bit/min and an accuracy of 74.84%. These results demonstrate that the use of automated interfaces to reduce complexity for the intended operator (outside the laboratory) is indeed possible. The signal processing and classifier source code embedded in BCI2000 is available from https://www.brain-project.org/downloads.html. © 2001-2011 IEEE.


Valbuena D.,Friedrich Wilhelm Bessel Institute
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference | Year: 2010

Brain-computer interface (BCI) systems enable communication and control without movement. Although advanced signal processing methods are used in BCI research, the output of a BCI is still unreliable, and the information transfer rates are very low compared with conventional human interaction interfaces such as keyboard and mouse. Therefore, improvements in signal classification methods and the exploitation of the learning skills of the user are required to compensate the unreliability of the BCI system. This work analyzes the response time of the Bremen-BCI based on steady-state visual evoked potentials (SSVEP) previously tested on 27 subjects, and presents an enhanced method for faster detection of SSVEP responses. The aim is toward the development of a swift BCI (sBCI) that robustly detects the exact time point where the user starts modulating his brain signals.


PubMed | Friedrich Wilhelm Bessel Institute
Type: | Journal: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference | Year: 2010

Brain-computer interface (BCI) systems enable communication and control without movement. Although advanced signal processing methods are used in BCI research, the output of a BCI is still unreliable, and the information transfer rates are very low compared with conventional human interaction interfaces such as keyboard and mouse. Therefore, improvements in signal classification methods and the exploitation of the learning skills of the user are required to compensate the unreliability of the BCI system. This work analyzes the response time of the Bremen-BCI based on steady-state visual evoked potentials (SSVEP) previously tested on 27 subjects, and presents an enhanced method for faster detection of SSVEP responses. The aim is toward the development of a swift BCI (sBCI) that robustly detects the exact time point where the user starts modulating his brain signals.

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