%0 Book Section %B Brain-Computer Interface Research: A State-of-the-Art Summary %D 2015 %T Towards an Auditory Attention BCI %A Peter Brunner %A Dijkstra, K. %A Coon, W.G. %A Mellinger, Jürgen %A A L Ritaccio %A Gerwin Schalk %X People affected by severe neuro-degenerative diseases (e.g., late-stage amyotrophic lateral sclerosis (ALS) or locked-in syndrome) eventually lose all muscular control and are no longer able to gesture or speak. For this population, an auditory BCI is one of only a few remaining means of communication. All currently used auditory BCIs require a relatively artificial mapping between a stimulus and a communication output. This mapping is cumbersome to learn and use. Recent studies suggest that electrocorticographic (ECoG) signals in the gamma band (i.e., 70–170 Hz) can be used to infer the identity of auditory speech stimuli, effectively removing the need to learn such an artificial mapping. However, BCI systems that use this physiological mechanism for communication purposes have not yet been described. In this study, we explore this possibility by implementing a BCI2000-based real-time system that uses ECoG signals to identify the attended speaker. %B Brain-Computer Interface Research: A State-of-the-Art Summary %I Springer International Publishing %C New York City, NY %P 29-42 %@ 978-3-319-25188-2 %G eng %U http://link.springer.com/chapter/10.1007%2F978-3-319-25190-5_4 %R 10.1007/978-3-319-25190-5_4 %0 Book %D 2010 %T A Practical Guide to Brain-Computer Interfacing with BCI2000. %A Gerwin Schalk %A Mellinger, Jürgen %I Springer London Dordrecht Heidelberg New York %P 288 %G eng %U http://link.springer.com/book/10.1007%2F978-1-84996-092-2 %R 10.1007/978-1-84996-092-2 %0 Journal Article %J IEEE Trans Biomed Eng %D 2010 %T A procedure for measuring latencies in brain-computer interfaces. %A Adam J Wilson %A Mellinger, Jürgen %A Gerwin Schalk %A Williams, Justin C %K Brain %K Computer Systems %K Electroencephalography %K Evoked Potentials %K Humans %K Models, Neurological %K Reproducibility of Results %K Signal Processing, Computer-Assisted %K Time Factors %K User-Computer Interface %X

Brain-computer interface (BCI) systems must process neural signals with consistent timing in order to support adequate system performance. Thus, it is important to have the capability to determine whether a particular BCI configuration (i.e., hardware and software) provides adequate timing performance for a particular experiment. This report presents a method of measuring and quantifying different aspects of system timing in several typical BCI experiments across a range of settings, and presents comprehensive measures of expected overall system latency for each experimental configuration.

%B IEEE Trans Biomed Eng %V 57 %P 1785-97 %8 06/2010 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/20403781 %N 7 %R 10.1109/TBME.2010.2047259 %0 Book Section %B Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction %D 2010 %T Using BCI2000 in BCI Research. %A Mellinger, Jürgen %A Gerwin Schalk %E Graimann, Bernhard %E Pfurtscheller, Gert %E Brendan Z. Allison %X

BCI2000 is a general-purpose system for brain–computer interface (BCI) research. It can also be used for data acquisition, stimulus presentation, and brain monitoring applications [18,27]. The mission of the BCI2000 project is to facilitate research and applications in these areas. BCI2000 has been in development since 2000 in a collaboration between the Wadsworth Center of the New York State Department of Health in Albany, New York, and the Institute of Medical Psychology and Behavioral Neurobiology at the University of Tübingen, Germany. Many other individuals at different institutions world-wide have contributed to this project.

%B Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction %S The Frontiers Collection %I Springer Berlin Heidelberg %P 259-280 %G eng %U http://dx.doi.org/10.1007/978-3-642-02091-9_15 %0 Journal Article %J Journal of neuroscience methods %D 2008 %T An auditory brain-computer interface (BCI). %A Nijboer, Femke %A Adrian Furdea %A Gunst, Ingo %A Mellinger, Jürgen %A Dennis J. McFarland %A Niels Birbaumer %A Kübler, Andrea %K auditory feedback %K brain-computer interface %K EEG %K locked-in state %K motivation %K sensorimotor rhythm %X Brain-computer interfaces (BCIs) translate brain activity into signals controlling external devices. BCIs based on visual stimuli can maintain communication in severely paralyzed patients, but only if intact vision is available. Debilitating neurological disorders however, may lead to loss of intact vision. The current study explores the feasibility of an auditory BCI. Sixteen healthy volunteers participated in three training sessions consisting of 30 2-3 min runs in which they learned to increase or decrease the amplitude of sensorimotor rhythms (SMR) of the EEG. Half of the participants were presented with visual and half with auditory feedback. Mood and motivation were assessed prior to each session. Although BCI performance in the visual feedback group was superior to the auditory feedback group there was no difference in performance at the end of the third session. Participants in the auditory feedback group learned slower, but four out of eight reached an accuracy of over 70% correct in the last session comparable to the visual feedback group. Decreasing performance of some participants in the visual feedback group is related to mood and motivation. We conclude that with sufficient training time an auditory BCI may be as efficient as a visual BCI. Mood and motivation play a role in learning to use a BCI. %B Journal of neuroscience methods %V 167 %P 43–50 %8 01/2008 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/17399797 %R 10.1016/j.jneumeth.2007.02.009 %0 Book Section %B Brain-Computer Interfaces %D 2007 %T BCI2000: A General-Purpose Software Platform for BCI Research. %A Mellinger, Jürgen %A Gerwin Schalk %B Brain-Computer Interfaces %I MIT Press %G eng %0 Book Section %D 2007 %T Brain Computer Interfaces for Communication in Paralysis: a Clinical-Experimental Approach. %A Hinterberger, T. %A Nijboer, F %A Kübler, A. %A Matuz, T. %A Adrian Furdea %A Mochty, Ursula %A Jordan, M. %A Lal, T.N %A Jeremy Jeremy Hill %A Mellinger, Jürgen %A Bensch, M %A Tangermann, Michael %A Widmann, G. %A Elger, Christian %A Rosenstiel, W. %A Schölkopf, B %A Niels Birbaumer %K brain-computer interfaces %K EEG %K experiment %K Medical sciences Medicine %K paralyzed patients %K slow cortical potentials %K Thought-Translation Device %X

An overview of different approaches to brain-computer interfaces (BCIs) developed in our laboratory is given. An important clinical application of BCIs is to enable communication or environmental control in severely paralyzed patients. The BCI “Thought-Translation Device (TTD)” allows verbal communication through the voluntary self-regulation of brain signals (e.g., slow cortical potentials (SCPs)), which is achieved by operant feedback training. Humans' ability to self-regulate their SCPs is used to move a cursor toward a target that contains a selectable letter set. Two different approaches were followed to developWeb browsers that could be controlled with binary brain responses. Implementing more powerful classification methods including different signal parameters such as oscillatory features improved our BCI considerably. It was also tested on signals with implanted electrodes. Most BCIs provide the user with a visual feedback interface. Visually impaired patients require an auditory feedback mode. A procedure using auditory (sonified) feedback of multiple EEG parameters was evaluated. Properties of the auditory systems are reported and the results of two experiments with auditory feedback are presented. Clinical data of eight ALS patients demonstrated that all patients were able to acquire efficient brain control of one of the three available BCI systems (SCP, µ-rhythm, and P300), most of them used the SCP-BCI. A controlled comparison of the three systems in a group of ALS patients, however, showed that P300-BCI and the µ-BCI are faster and more easily acquired than SCP-BCI, at least in patients with some rudimentary motor control left. Six patients who started BCI training after entering the completely locked-in state did not achieve reliable communication skills with any BCI system. One completely locked-in patient was able t o communicate shortly with a ph-meter, but lost control afterward.

%I Virtual Library of Psychology at Saarland University and State Library, GERMANY, PsyDok [http://psydok.sulb.uni-saarland.de/phpoai/oai2.php] (Germany) %@ 9780262256049 %G eng %U http://psydok.sulb.uni-saarland.de/volltexte/2008/2154/ %0 Journal Article %J Neuroimage %D 2007 %T An MEG-based brain-computer interface (BCI). %A Mellinger, Jürgen %A Gerwin Schalk %A Christoph Braun %A Preissl, Hubert %A Rosenstiel, W. %A Niels Birbaumer %A Kübler, A. %K Adult %K Algorithms %K Artifacts %K Brain %K Electroencephalography %K Electromagnetic Fields %K Electromyography %K Feedback %K Female %K Foot %K Hand %K Head Movements %K Humans %K Magnetic Resonance Imaging %K Magnetoencephalography %K Male %K Movement %K Principal Component Analysis %K Signal Processing, Computer-Assisted %K User-Computer Interface %X

Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography(EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor mu and beta rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant mu rhythm self control within 32 min of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.

%B Neuroimage %V 36 %P 581-93 %8 07/2007 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/17475511 %N 3 %R 10.1016/j.neuroimage.2007.03.019 %0 Journal Article %J IEEE Trans Neural Syst Rehabil Eng %D 2006 %T BCI meeting 2005 - Workshop on Technology: Hardware and Software. %A Cincotti, F %A Bianchi, L %A Birch, Gary %A Guger, C %A Mellinger, Jürgen %A Scherer, Reinhold %A Schmidt, Robert N %A Yáñez Suárez, Oscar %A Gerwin Schalk %K Algorithms %K Biotechnology %K Brain %K Communication Aids for Disabled %K Computers %K Electroencephalography %K Equipment Design %K Humans %K Internationality %K Man-Machine Systems %K Neuromuscular Diseases %K Software %K User-Computer Interface %X

This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop to review and evaluate the current state of BCI-related hardware and software. Technical requirements and current technologies, standardization procedures and future trends are covered. The main conclusion was recognition of the need to focus technical requirements on the users' needs and the need for consistent standards in BCI research.

%B IEEE Trans Neural Syst Rehabil Eng %V 14 %P 128-31 %8 06/2006 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/16792276 %N 2 %R 10.1109/TNSRE.2006.875584 %0 Journal Article %D 2005 %T A Brain Computer Interface with Online Feedback based on Magnetoencephalography. %A Lal, T.N %A Schroeder, Michael %A Jeremy Jeremy Hill %A Preissl, Hubert %A Hinterberger, T. %A Mellinger, Jürgen %A Bogdan, Martin %A Rosenstiel, W. %A Niels Birbaumer %A Schoelkopf, Bernhard %K Brain Computer Interfaces %K User Modelling for Computer Human Interaction %X

The aim of this paper is to show that machine learning techniques can be used to derive a classifying function for human brain signal data measured by magnetoencephalography (MEG), for the use in a brain computer interface (BCI). This is especially helpful for evaluating quickly whether a BCI approach based on electroencephalography, on which training may be slower due to lower signalto-noise ratio, is likely to succeed. We apply RCE and regularized SVMs to the experimental data of ten healthy subjects performing a motor imagery task. Four subjects were able to use a trained classifier to write a short name. Further analysis gives evidence that the proposed imagination task is suboptimal for the possible extension to a multiclass interface. To the best of our knowledge this paper is the first working online MEG-based BCI and is therefore a “proof of concept”.

%8 08/2005 %G eng %U http://www.researchgate.net/publication/221346004_A_brain_computer_interface_with_online_feedback_based_on_magnetoencephalography %R 10.1145/1102351.1102410 %0 Journal Article %J Neurology %D 2005 %T Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. %A Kübler, A. %A Nijboer, F %A Mellinger, Jürgen %A Theresa M Vaughan %A Pawelzik, H %A Gerwin Schalk %A Dennis J. McFarland %A Niels Birbaumer %A Jonathan Wolpaw %K Aged %K Amyotrophic Lateral Sclerosis %K Electroencephalography %K Evoked Potentials, Motor %K Evoked Potentials, Somatosensory %K Female %K Humans %K Imagination %K Male %K Middle Aged %K Motor Cortex %K Movement %K Paralysis %K Photic Stimulation %K Prostheses and Implants %K Somatosensory Cortex %K Treatment Outcome %K User-Computer Interface %X

People with severe motor disabilities can maintain an acceptable quality of life if they can communicate. Brain-computer interfaces (BCIs), which do not depend on muscle control, can provide communication. Four people severely disabled by ALS learned to operate a BCI with EEG rhythms recorded over sensorimotor cortex. These results suggest that a sensorimotor rhythm-based BCI could help maintain quality of life for people with ALS.

%B Neurology %V 64 %P 1775-7 %8 05/2005 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/15911809 %N 10 %R 10.1212/01.WNL.0000158616.43002.6D %0 Journal Article %J Biomedizinische Technik %D 2004 %T P300 for communication: Evidence from patients with amyotrophic lateral sclerosis (ALS). %A Mellinger, Jürgen %A Nijboer, F %A Pawelzik, H %A Gerwin Schalk %A Dennis J. McFarland %A Theresa M Vaughan %A Jonathan Wolpaw %A Niels Birbaumer %A Kuebler, A. %B Biomedizinische Technik %G eng