Niels Birbaumer

University of Tuebingen, Tübingen, Baden-Württemberg, Germany

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Publications (463)1569.92 Total impact

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    Ujwal Chaudhary, Niels Birbaumer
    05/2015; 3(Suppl 1):S29. DOI:10.3978/j.issn.2305-5839.2015.02.27
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    DESCRIPTION: Individuals with autism spectrum disorder (ASD) demonstrate intact or superior local processing of visual-spatial tasks. We investigated the hypothesis that in a disembedding task, autistic individuals exhibit a more local processing style than controls, which is reflected by altered electromagnetic brain activity in response to embedded stimuli and enhanced activity of early visual areas. Ten autistic and ten matched control participants underwent 151-channel whole-head magnetoencephalography. Par- ticipants were presented with 400 embedded or isolated letters (‘S’ or ‘H’) and asked to indicate which of the two letters was shown. Performance was equal in both groups, but event-related magnetic fields differed between groups in an early (100–150 ms) and a later (350–400 ms) time window. In the early time window, autistic individuals differed from control participants in the embedded, but not in the iso- lated condition, reflecting reduced processing of the irrelevant context in autistic individuals. In the later time window, amplitude differences between the embedded and isolated conditions were measured in control participants only, suggesting that “disembedding” processes were not required in autistic individuals. Source localisation indicated that activity in individuals with ASD peaked in the primary visual cortex in both conditions and time windows indicating an effortless (automatic, bottom-up) local process, whereas activity in controls peaked outside the visual cortex.
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    ABSTRACT: The main objective of this roadmap is to provide a global perspective on the BCI field now and in the future. For readers not familiar with BCIs, we introduce basic terminology and concepts. We discuss what BCIs are, what BCIs can do, and who can benefit from BCIs. We illustrate our arguments with use cases to support the main messages. After reading this roadmap you will have a clear picture of the potential benefits and challenges of BCIs, the steps necessary to bridge the gap between current and future applications, and the potential impact of BCIs on society in the next decade and beyond.
  • Ander Ramos-Murguialday, Niels Birbaumer
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    ABSTRACT: Non-invasive brain-computer-interfaces (BCI) coupled with prosthetic devices were recently introduced in the rehabilitation of chronic stroke and other disorders of the motor system. These BCI systems and motor rehabilitation in general involve several motor tasks for training. This study investigates the neurophysiological bases of an EEG-oscillations-driven BCI combined with a neuroprosthetic device in order to define the specific oscillatory signature of the BCI-task. We recorded EEG while 9 healthy participants performed five different motor tasks consisting of closing and opening of the hand: 1) motor imagery without any external feedback and without overt hand movement, 2) motor imagery which moves the orthosis proportional to the produced brain oscillation change with online proprioceptive and visual feedback of the hand moving through a neuroprosthetic device (BCI-condition), 3) passive and 4) active movement of the hand with feedback (seeing and feeling the hand moving) and 5) rest. We analyzed brain activity during the 5 conditions using time-frequency domain bootstrap based statistical comparisons and Morlet transforms. Activity during rest condition was used as reference. Significant contralateral and ipsilateral event related desynchronization of sensorimotor rhythm was present during all motor tasks, largest in contralateral-post-central, medio-central and ipsilateral-pre-central areas identifying the ipsilateral pre-central cortex as an integral part of motor regulation. Changes in task specific frequency power when compared to rest were similar between motor tasks and only significant differences in the time course and some narrow specific frequency bands were observed between motor tasks. We identified EEG features representing proprioception, active intention and passive involvement differentiating brain oscillations during motor tasks that could substantially support the design of novel motor BCI-based rehabilitation therapies. The BCI task induced significantly different brain activity compared to the other motor tasks indicating neural processes unique to the use of body actuators control in a BCI-context. Copyright © 2013, Journal of Neurophysiology.
    Journal of Neurophysiology 03/2015; 113(10):jn.00467.2013. DOI:10.1152/jn.00467.2013 · 3.04 Impact Factor
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    ABSTRACT: Psychopathic individuals are characterized by impaired affective processing, impulsivity, sensation-seeking, poor planning skills and heightened aggressiveness with poor self-regulation. Based on brain self-regulation studies using neurofeedback of Slow Cortical Potentials (SCPs) in disorders associated with a dysregulation of cortical activity thresholds and evidence of deficient cortical functioning in psychopathy, a neurobiological approach seems to be promising in the treatment of psychopathy. The results of our intensive brain regulation intervention demonstrate, that psychopathic offenders are able to gain control of their brain excitability over fronto-central brain areas. After SCP self-regulation training, we observed reduced aggression, impulsivity and behavioral approach tendencies, as well as improvements in behavioral-inhibition and increased cortical sensitivity for error-processing. This study demonstrates improvements on the neurophysiological, behavioral and subjective level in severe psychopathic offenders after SCP-neurofeedback training and could constitute a novel neurobiologically-based treatment for a seemingly change-resistant group of criminal psychopaths.
    Scientific Reports 03/2015; 5. DOI:10.1038/srep09426 · 5.58 Impact Factor
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    ABSTRACT: Task performance depends on ongoing brain activity which can be influenced by attention, arousal, or motivation. However, such modulating factors of cognitive efficiency are unspecific, can be difficult to control, and are not suitable to facilitate neural processing in a regionally specific manner. Here, we non-pharmacologically manipulated regionally specific brain activity using technically sophisticated real-time fMRI neurofeedback. This was accomplished by training participants to simultaneously control ongoing brain activity in circumscribed motor and memory-related brain areas, namely the supplementary motor area and the parahippocampal cortex. We found that learned voluntary control over these functionally distinct brain areas caused functionally specific behavioral effects, i.e. shortening of motor reaction times and specific interference with memory encoding. The neurofeedback approach goes beyond improving cognitive efficiency by unspecific psychological factors such as attention, arousal, or motivation. It allows for directly manipulating sustained activity of task-relevant brain regions in order to yield specific behavioral or cognitive effects. Copyright © 2015. Published by Elsevier B.V.
    Biological psychology 03/2015; 220. DOI:10.1016/j.biopsycho.2015.03.009 · 3.47 Impact Factor
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    ABSTRACT: Background. Eye trackers are widely used among people with amyotrophic lateral sclerosis, and their benefits to quality of life have been previously shown. On the contrary, Brain-computer interfaces (BCIs) are still quite a novel technology, which also serves as an access technology for people with severe motor impairment. Objective. To compare a visual P300-based BCI and an eye tracker in terms of information transfer rate (ITR), usability, and cognitive workload in users with motor impairments. Methods. Each participant performed 3 spelling tasks, over 4 total sessions, using an Internet browser, which was controlled by a spelling interface that was suitable for use with either the BCI or the eye tracker. At the end of each session, participants evaluated usability and cognitive workload of the system. Results. ITR and System Usability Scale (SUS) score were higher for the eye tracker (Wilcoxon signed-rank test: ITR T = 9, P = .016; SUS T = 12.50, P = .035). Cognitive workload was higher for the BCI (T = 4; P = .003). Conclusions. Although BCIs could be potentially useful for people with severe physical disabilities, we showed that the usability of BCIs based on the visual P300 remains inferior to eye tracking. We suggest that future research on visual BCIs should use eye tracking-based control as a comparison to evaluate performance or focus on nonvisual paradigms for persons who have lost gaze control. © The Author(s) 2015.
    Neurorehabilitation and neural repair 03/2015; DOI:10.1177/1545968315575611 · 4.62 Impact Factor
  • Brain Stimulation 03/2015; 8(2):436. DOI:10.1016/j.brs.2015.01.392 · 5.43 Impact Factor
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    ABSTRACT: The brain-computer interface (BCI) field has grown dramatically over the past few years, but there are still no coordinated efforts to ensure efficient communication and collaboration among key stakeholders. The European Commission (EC) has recently renewed their efforts to establish such a coordination effort by funding a coordination and support action for the BCI community called ‘BNCI Horizon 2020’ after the ‘Future BNCI’ project. Major goals of this new project include developing a roadmap for the next decade and beyond, encouraging discussion and collaboration within the BCI community, fostering communication with the general public, and the foundation of an international BCI Society. We present a short overview of current and past EU-funded BCI projects and provide evidence of a growing research and industrial community. Efficient communication also entails the establishment of clear terminology, which is a major goal of BNCI Horizon 2020. To this end, we give a brief overview of current BCI-related terms and definitions. A major networking activity in the project was the BNCI Horizon 2020 Retreat in Hallstatt, Austria. Over 60 experts participated in this event to discuss the future of the BCI field in a series of plenary talks, targeted discussions, and parallel focus sessions. A follow-up event was the EU BCI Day at the 6th International Brain-Computer Interface Conference in Graz, Austria. This networking event included plenary talks by eight companies and representatives from all seven ongoing EU research projects, poster presentations, demos, and discussions. Another goal of BNCI Horizon 2020 is the foundation of an official BCI Society. In this article, we summarize the current status of this process. Finally, we present visions for future BCI applications developed within BNCI Horizon 2020 using input from external BCI experts as well. We identify common themes and conclude with six exemplary use cases.
    02/2015; DOI:10.1080/2326263X.2015.1008956
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    ABSTRACT: Stroke is one of the leading causes for severe adult long-term disability. The number of people who depend on assistance in their daily life activities has drastically increased over the last years and will further accumulate due to demographic factors. Besides impact on cognitive and affective brain function, motor paralysis is the heaviest burden of stroke. While recent studies demonstrated the human brain’s remarkable capacity to reorganize and restore function under effective learning conditions, most rehabilitation strategies require residual movements that, however, are lacking in up to 30–50 % of stroke survivors. For these patients, there is currently no standardized or accepted treatment strategy. Recently it was shown that brain–machine interfaces (BMI) translating electric or metabolic brain signals into control signals of computers or machines provide two strategies that play an increasing role for the recovery of these stroke survivors’ motor function: first, assistive BMIs striving for continuous high-dimensional brain control of robotic devices or functional electric stimulation (FES) to assist in performing daily life activities and, second, rehabilitative BMIs aiming at augmentation of neuroplasticity facilitating recovery of brain function. Recent demonstrations of such assistive and rehabilitative BMI system’s clinical applicability, safety, and efficacy suggest that BMIs will play a substantial role in rehabilitation strategies for severe motor paralysis after stroke.
    Clinical Systems Neuroscience, 01/2015: pages 3-14; , ISBN: 978-4-431-55036-5
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    ABSTRACT: Objective Stroke is a leading cause of long-term motor disability. Stroke patients with severe hand weakness do not profit from rehabilitative treatments. Recently, brain-controlled robotics and sequential functional electrical stimulation allowed some improvement. However, for such therapies to succeed, it is required to decode patients' intentions for different arm movements. Here, we evaluated whether residual muscle activity could be used to predict movements from paralyzed joints in severely impaired chronic stroke patients. Methods Muscle activity was recorded with surface-electromyography (EMG) in 41 patients, with severe hand weakness (Fugl-Meyer Assessment [FMA] hand subscores of 2.93 ± 2.7), in order to decode their intention to perform six different motions of the affected arm, required for voluntary muscle activity and to control neuroprostheses. Decoding of paretic and nonparetic muscle activity was performed using a feed-forward neural network classifier. The contribution of each muscle to the intended movement was determined. Results Decoding of up to six arm movements was accurate (>65%) in more than 97% of nonparetic and 46% of paretic muscles. Interpretation These results demonstrate that some level of neuronal innervation to the paretic muscle remains preserved and can be used to implement neurorehabilitative treatments in 46% of patients with severe paralysis and extensive cortical and/or subcortical lesions. Such decoding may allow these patients for the first time after stroke to control different motions of arm prostheses through muscle-triggered rehabilitative treatments.
    01/2015; 7. DOI:10.1002/acn3.122
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    ABSTRACT: The loss of hand function can result in severe physical and psychosocial impairment. Thus, compensation of a lost hand function using assistive robotics that can be operated in daily life is very desirable. However, versatile, intuitive, and reliable control of assistive robotics is still an unsolved challenge. Here, we introduce a novel brain/neural-computer interaction (BNCI) system that integrates electroencephalography (EEG) and electrooculography (EOG) to improve control of assistive robotics in daily life environments. To evaluate the applicability and performance of this hybrid approach, five healthy volunteers (HV) (four men, average age 26.5±3.8 years) and a 34-year-old patient with complete finger paralysis due to a brachial plexus injury (BPI) used EEG (condition 1) and EEG/EOG (condition 2) to control grasping motions of a hand exoskeleton. All participants were able to control the BNCI system (BNCI control performance HV: 70.24±16.71%, BPI: 65.93±24.27%), but inclusion of EOG significantly improved performance across all participants (HV: 80.65±11.28, BPI: 76.03±18.32%). This suggests that hybrid BNCI systems can achieve substantially better control over assistive devices, e.g., a hand exoskeleton, than systems using brain signals alone and thus may increase applicability of brain-controlled assistive devices in daily life environments.
    Biomedizinische Technik/Biomedical Engineering 12/2014; 60(3). DOI:10.1515/bmt-2014-0126 · 2.43 Impact Factor
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    ABSTRACT: Introduction: Different techniques for neurofeedback of voluntary brain activations are currently being explored for clinical application in brain disorders. One of the most frequently used approaches is the self-regulation of oscillatory signals recorded with electroencephalography (EEG). Many patients are, however, unable to achieve sufficient voluntary control of brain activity. This could be due to the specific anatomical and physiological changes of the patient’s brain after the lesion, as well as to methodological issues related to the technique chosen for recording brain signals. Methods: A patient with an extended ischemic lesion of the cortex did not gain volitional control of sensorimotor oscillations when using a standard EEG-based approach. We provided him with neurofeedback of his brain activity from the epidural space by electrocorticography (ECoG). Results: Ipsilesional epidural recordings of field potentials facilitated self-regulation of brain oscillations in an online closed-loop paradigm and allowed reliable neurofeedback training for a period of 4 weeks. Conclusion: Epidural implants may decode and train brain activity even when the cortical physiology is distorted following severe brain injury. Such practice would allow for reinforcement learning of preserved neural networks and may well provide restorative tools for those patients who are severely afflicted.
    Frontiers in Behavioral Neuroscience 12/2014; 8. DOI:10.3389/fnbeh.2014.00429 · 4.16 Impact Factor
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    ABSTRACT: Stroke is among the leading causes of long-term disabilities leaving an increasing number of people with cognitive, affective and motor impairments depending on assistance in their daily life. While function after stroke can significantly improve in the first weeks and months, further recovery is often slow or non-existent in the more severe cases encompassing 30-50% of all stroke victims. The neurobiological mechanisms underlying recovery in those patients are incompletely understood. However, recent studies demonstrated the brain's remarkable capacity for functional and structural plasticity and recovery even in severe chronic stroke. As all established rehabilitation strategies require some remaining motor function, there is currently no standardized and accepted treatment for patients with complete chronic muscle paralysis. The development of brain-machine interfaces (BMIs) that translate brain activity into control signals of computers or external devices provides two new strategies to overcome stroke-related motor paralysis. First, BMIs can establish continuous high-dimensional brain-control of robotic devices or functional electric stimulation (FES) to assist in daily life activities (assistive BMI). Second, BMIs could facilitate neuroplasticity, thus enhancing motor learning and motor recovery (rehabilitative BMI). Advances in sensor technology, development of non-invasive and implantable wireless BMI-systems and their combination with brain stimulation, along with evidence for BMI system's clinical efficacy suggest that BMI-related strategies will play an increasing role in neurorehabilitation of stroke. Copyright © 2014. Published by Elsevier Inc.
    Neurobiology of Disease 12/2014; DOI:10.1016/j.nbd.2014.11.025 · 5.20 Impact Factor
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    ABSTRACT: The combination of Brain-Computer Interface (BCI) technology, allowing online monitoring and decoding of brain activity, with virtual and mixed reality (MR) systems may help to shape and guide implicit and explicit learning using ecological scenarios. Real-time information of ongoing brain states acquired through BCI might be exploited for controlling data presentation in virtual environments. Brain states discrimination during mixed reality experience is thus critical for adapting specific data features to contingent brain activity. In this study we recorded electroencephalographic (EEG) data while participants experienced MR scenarios implemented through the eXperience Induction Machine (XIM). The XIM is a novel framework modeling the integration of a sensing system that evaluates and measures physiological and psychological states with a number of actuators and effectors that coherently reacts to the user's actions. We then assessed continuous EEG-based discrimination of spatial navigation, reading and calculation performed in MR, using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Dynamic single trial classification showed high accuracy of LDA and SVM classifiers in detecting multiple brain states as well as in differentiating between high and low mental workload, using a 5 s time-window shifting every 200 ms. Our results indicate overall better performance of LDA with respect to SVM and suggest applicability of our approach in a BCI-controlled MR scenario. Ultimately, successful prediction of brain states might be used to drive adaptation of data representation in order to boost information processing in MR.
    Frontiers in Behavioral Neuroscience 11/2014; 8(415). DOI:10.3389/fnbeh.2014.00415 · 4.16 Impact Factor

Publication Stats

21k Citations
1,569.92 Total Impact Points


  • 1977–2015
    • University of Tuebingen
      • • Institute of Medical Psychology and Behavioral Neurobiology
      • • Department of Psychology
      Tübingen, Baden-Württemberg, Germany
  • 2013–2014
    • Fondazione Ospedale San Camillo, Venezia
      Venetia, Veneto, Italy
  • 2011–2013
    • Ospedale di San Raffaele Istituto di Ricovero e Cura a Carattere Scientifico
      Milano, Lombardy, Italy
    • Institut Philippe-Pinel de Montréal
      Montréal, Quebec, Canada
  • 2012
    • Boca Raton Regional Hospital
      Boca Raton, Florida, United States
  • 2007–2011
    • National Institutes of Health
      • National Institute of Neurological Disorders and Stroke (NINDS)
      Bethesda, MD, United States
    • University of Arkansas for Medical Sciences
      Little Rock, Arkansas, United States
  • 2010
    • University of Zaragoza
      Caesaraugusta, Aragon, Spain
  • 2008
    • University of Wuerzburg
      Würzburg, Bavaria, Germany
  • 1988–2007
    • Pennsylvania State University
      • Department of Psychology
      State College, PA, United States
  • 2006
    • Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.
      München, Bavaria, Germany
    • Max Planck Institute for Biological Cybernetics
      • Department of Human Perception, Cognition and Action
      Tübingen, Baden-Württemberg, Germany
    • University of Bonn
      Bonn, North Rhine-Westphalia, Germany
  • 2002–2006
    • Università degli Studi di Trento
      Trient, Trentino-Alto Adige, Italy
  • 2005
    • University of Colorado at Boulder
      Boulder, Colorado, United States
  • 2001
    • Technische Universität Dresden
      • Fachrichtung Psychologie
      Dresden, Saxony, Germany
  • 1998
    • Humboldt-Universität zu Berlin
      Berlín, Berlin, Germany
  • 1994–1998
    • University of Padova
      • Department of General Psychology
      Padua, Veneto, Italy
  • 1997
    • Universität Konstanz
      • Department of Psychology
      Constance, Baden-Württemberg, Germany
  • 1992
    • Università degli Studi del Sannio
      Benevento, Campania, Italy
  • 1985
    • Yale University
      New Haven, Connecticut, United States
  • 1984
    • Universität Ulm
      • Institute of Clinical and Biological Psychology
      Ulm, Baden-Württemberg, Germany