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ABSTRACT: Brain-computer 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.
IEEE Transactions on Neural Systems and Rehabilitation Engineering 07/2011; · 3.44 Impact Factor
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ABSTRACT: Brain-computer 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.
IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society 03/2011; 19(3):232-9. · 2.42 Impact Factor
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ABSTRACT: Modern brain-computer interface (BCI) systems use different types of neural activity for control. Most BCI systems only allow the customization of very few parameters and focus only on one type of BCI approach. Many articles reported that a certain BCI did not work for some users (so called BCI illiteracy). We are introducing the BCI wizard as a system that automatically identifies key parameters to customize the best BCI paradigm for each user. With a BCI wizard it is possible to develop an interface that relies on the best mental strategy for each user and therefore makes the difference between an ineffective system and a working BCI. This work presents a preliminary study that aims to develop a BCI wizard exploring the two most effective BCI approaches (SSVEP and P300). These types of non-invasive BCIs were tested and evaluated in a group of 14 healthy subjects. During online tests all subjects were asked to spell three words with two spelling applications and at the end of the experiment they chose their preferred approach. Results showed that all subjects could communicate with the P300-based BCI with an accuracy above 69% (5 reached 100% accuracy), 10 out of 14 subjects could effectively use the SSVEP-based BCI (2 reached 100% accuracy). These promising results confirm that BCI wizard will enable BCIs customized to each user with considerably greater flexibility and independence than present systems allow.
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010
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ABSTRACT: 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.
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010
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ABSTRACT: A brain-computer interface (BCI) based on steady-state visual-evoked potentials (SSVEPs) is combined with a functional electrical stimulation (FES) system to allow the user to control stimulation settings and parameters. The system requires four flickering lights of distinct frequencies that are used to form a menu-based interface, enabling the user to interact with the FES system. The approach was evaluated in 12 neurologically intact subjects to change the parameters and operating mode of an abdominal stimulation system for respiratory assistance. No major influence of the FES on the raw EEG signal could be observed. In tests with a self-paced task, a mean accuracy of more than 90% was achieved, with detection times of approximately 7.7 s and an average information transfer rate of 12.5 bits/min. There was no significant dependency of the accuracy or time of detection on the FES stimulation intensity. The results indicate that the system could be used to control FES-based neuroprostheses with a high degree of accuracy and robustness.
IEEE Transactions on Biomedical Engineering 09/2010; · 2.28 Impact Factor
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ABSTRACT: Brain-computer interface (BCI) systems enable communication without movement. It is unclear why some BCI approaches or parameters are less effective with some users. This study elucidates BCI demographics by exploring correlations among BCI performance, personal preferences, and different subject factors such as age or gender. Results showed that most people, despite having no prior BCI experience, could use the Bremen SSVEP BCI system in a very noisy field setting. Performance tended to be better in both young and female subjects. Most subjects stated that they did not consider the flickering stimuli annoying and would use or recommend this BCI system. These and other demographic analyses may help identify the best BCI for each user.
IEEE Transactions on Neural Systems and Rehabilitation Engineering 05/2010; · 3.44 Impact Factor
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[show abstract]
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ABSTRACT: A brain-computer interface (BCI) based on steady-state visual-evoked potentials (SSVEPs) is combined with a functional electrical stimulation (FES) system to allow the user to control stimulation settings and parameters. The system requires four flickering lights of distinct frequencies that are used to form a menu-based interface, enabling the user to interact with the FES system. The approach was evaluated in 12 neurologically intact subjects to change the parameters and operating mode of an abdominal stimulation system for respiratory assistance. No major influence of the FES on the raw EEG signal could be observed. In tests with a self-paced task, a mean accuracy of more than 90% was achieved, with detection times of approximately 7.7 s and an average information transfer rate of 12.5 bits/min. There was no significant dependency of the accuracy or time of detection on the FES stimulation intensity. The results indicate that the system could be used to control FES-based neuroprostheses with a high degree of accuracy and robustness.
IEEE transactions on bio-medical engineering 02/2010; 57(8):1847-55. · 2.15 Impact Factor
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ABSTRACT: A brain-computer interface (BCI) provides the possibility to translate brain neural activity patterns into control commands without user's movement. The brain activity is most commonly measured non-invasively via standard electroencephalography (EEG), i.e., with electrodes placed on the surface of the scalp. In this article, we evaluate a BCI system based on steady-state visual evoked potentials (SSVEPs) in real world conditions. Although the performance of this type of BCI has already been proved by several research groups with healthy users in laboratory settings assisted by scientific researchers, there are still many difficulties in changing from demonstration systems to practical BCIs. The Bremen-BCI was evaluated in this case study with 37 naive subjects (without any SSVEP-BCI experience), including 8 handicapped users, at the international rehabilitation fair RehaCare2008. In spite of unsuitable environment conditions on the fair, the spelling tasks were successfully completed by 32 participants with a mean accuracy of over 92% and an average information transfer rate (ITR) of 22.6[bits/min]. No significant dependency of the physical disability of participants on the ITR could be observed.
Rehabilitation Robotics, 2009. ICORR 2009. IEEE International Conference on; 07/2009
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ABSTRACT: In this work, different stimulation frequencies for steady-state visual evoked potentials (SSVEP) based brain-computer interface (BCI) were evaluated in a spelling task with the Bremen-BCI system. The classical two dimensional BCI control requires five classes: four classes are dedicated to the directions (up, down, left and right) and one class for action (select). The number of producible frequencies on the standard liquid crystal display (LCD) is limited due to the vertical refresh rate of 60 Hz and the number of simultaneously used stimuli. In order to find optimal stimulation frequencies, the Bremen-BCI was evaluated in the case study with 37 naive subjects (without any SSVEP-BCI experience), including eight handicapped users, at the international rehabilitation fair RehaCare2008. During online spelling task, subjects spelled few words with the Bremen-BCI system and the timings for classifying different flickering frequencies have been investigated. The fastest SSVEP response was achieved for the stimulus frequency of 6.67 Hz.
Neural Engineering, 2009. NER '09. 4th International IEEE/EMBS Conference on; 06/2009
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ABSTRACT: A brain-computer interface (BCI) provides the possibility to translate brain neural activity patterns into control commands for computers without user's movement. The brain activity is most commonly measured non-invasively via standard electroencephalographic (EEG) electrodes placed on the surface of the scalp. We propose the evaluation of the Bremen-BCI system based on steady-state visual evoked potentials (SSVEPs), which was evaluated with 37 BCI-naive subjects, including eight handicapped persons, on the international rehabilitation fair RehaCare2008. In spite of the noisy environment during the fair, the spelling tasks were successfully completed. We propose two evaluation methods, one based on the main task to achieve and the second, on the commands that are needed to achieve the task. In the command level, the mean accuracy of the command detection is 92.84%, with an average information transfer rate of 22.6 bpm (bits per minute). In the speller level, the average information transfer rate is 17.4 bpm (equivalent to 3.5 letters per minute with 30 possible letters). These results highlight the differences between two evaluation methods. Differences can emerge between the raw BCI and its connection to an application.
Neural Engineering, 2009. NER '09. 4th International IEEE/EMBS Conference on; 06/2009
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B. Allison, I. Volosyak,
T. Lüth,
D. Valbuena,
I. Sugiarto,
M. Spiegel,
A. Teymourian,
I.S. Condro,
A. Brindusescu,
K. Stenzel,
H. Cecotti,
A. Gräser
[show abstract]
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ABSTRACT: Brain-computer interface (BCI) systems enable communication without movement. It is unclear why some BCI approaches or parameters are less effective with some users. This study elucidates BCI demographics by exploring correlations among BCI performance, personal preferences and different subject factors such as age or gender. Results showed that most people despite having no prior BCI experience, could use the Bremen SSVEP BCI system in a very noisy field setting. Performance was best in both young and female subjects. Most subjects, especially younger subjects, stated that they did not consider the flickering stimuli annoying and would use or recommend this BCI system. These and other demographic analyses may help identify the best BCI for each user.
International Brain-Computer Interface Workshop and Training Course, Graz, Austria; 01/2008
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ABSTRACT: In this work, a brain-computer interface (BCI) based on steady-state visual evoked potentials (SSVEP) is presented as an input device for the human machine interface (HMI) of the semi-autonomous robot FRIEND II. The role of the BCI is to translate high-level requests from the user into control commands for the FRIEND II system. In the current application, the BCI is used to navigate a menu system and to select commands such as pouring a beverage into a glass. The low-level control of the test platform, the rehabilitation robot FRIEND II, is executed by the control architecture MASSiVE, which in turn is served by a planning instance, an environment model and a set of sensors (e.g., machine vision) and actors. The BCI is introduced as a step towards the goal of providing disabled users with at least 1.5 hours independence from care givers.
Rehabilitation Robotics, 2007. ICORR 2007. IEEE 10th International Conference on; 07/2007
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ABSTRACT: In this work, the real-time performance of a novel method for detecting steady-state visual evoked potentials (SSVEP) is evaluated in a brain-computer interface (BCI) spelling task. At the core of this method is a spatial filtering algorithm for extracting SSVEP responses, which in previous off-line studies has shown significantly improved classification performance. The on-line performance is investigated by letting a group of 11 healthy subjects spell the word `BRAINCOMPUTERINTERFACE'. An average information transfer rate of 27 bits/minute was obtained in this task and the probability of correctly classifying the user's intention was estimated to 97.5%. In addition, two different letter layouts and selection schemes tailored for SSVEP BCI's are compared.
Neural Engineering, 2007. CNE '07. 3rd International IEEE/EMBS Conference on; 06/2007
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ABSTRACT: In this paper, novel methods for detecting steady-state visual evoked potentials using multiple electroencephalogram (EEG) signals are presented. The methods are tailored for brain-computer interfacing, where fast and accurate detection is of vital importance for achieving high information transfer rates. High detection accuracy using short time segments is obtained by finding combinations of electrode signals that cancel strong interference signals in the EEG data. Data from a test group consisting of 10 subjects are used to evaluate the new methods and to compare them to standard techniques. Using 1-s signal segments, six different visual stimulation frequencies could be discriminated with an average classification accuracy of 84%. An additional advantage of the presented methodology is that it is fully online, i.e., no calibration data for noise estimation, feature extraction, or electrode selection is needed
IEEE Transactions on Biomedical Engineering 05/2007; · 2.28 Impact Factor
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ABSTRACT: The research and development in the field of rehabilitation robots produced a multiplicity of rehabilitation robots available
as off-the- shelf products or laboratory prototypes (e.g. [1]). The application scope of these systems is large and covers
ranges such as support for everyday tasks, assistance in the vocational surroundings or support in health care. An in-depth
analysis unveils that rehabilitation robots, which are intended for a flexible use but not for individual special applications,
offer services only on a relatively low level of abstraction, i.e. the direct low level control of the system [2] remains
by the user. This leads to a high cognitive load with accompanying concentration loss, especially for persons depending on
interfaces like speech control or eye movement trackers. In order to relieve the users from this kind of tiresome control
the treatment of tasks on higher abstraction level becomes desirable [3]. The system should be able to perform chains of actions,
which are repeated during daily life tasks, autonomously and/or with minimum necessary user interaction.
08/2006: pages 95-126;
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ABSTRACT: The main aim behind the design of rehabilitation robotic systems is to support disabled people in daily life situations as well as in the working environment. This requires the autonomous execution of different tasks. In order to achieve the ability of the robotic system to operate autonomously, the sensor system for the scene observation providing the necessary inputs for the manipulator control is essential. In this paper, the main emphasis is on the development and integration of the closed-loop controls at the visual sensory input level in order to increase the robustness of the vision-based system control. Moreover, the integration of additional sensors in order to support the vision system and to increase the reliability of the whole robotic system is discussed.
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) 03/2005; · 2.01 Impact Factor
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atp international-automation technology in practice. 01/2005; 2005(1):61-70.
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ABSTRACT: FRIEND II which is the successor of the rehabilitation robot FRIEND I is being developed at the Institute of Automation, University of Bremen, and belongs to the category 'intelligent' wheelchair mounted manipulators. Both systems are used as a personal assistant to support disabled persons with upper-limb impairments in daily life situations as well as in the working environment. Significant improvements are obtained with the use of smart devices, new camera systems, a humanlike robot arm with 7-joint kinematics, a new control concept - kinematic configuration control, force torque sensor and two interchangeable grippers. This paper describes the hardware selection and the innovations in the hardware of the system FRIEND II currently under development.
Rehabilitation Robotics, 2005. ICORR 2005. 9th International Conference on;
