Moritz Grosse-Wentrup

Max-Planck-Institut für Intelligente Systeme, Tübingen, Tübingen, Baden-Württemberg, Germany

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Publications (42)72.59 Total impact

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    ABSTRACT: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in its clinical presentation. This makes diagnosis and effective treatment difficult, so better tools for estimating disease progression are needed. Here, we report results from the DREAM-Phil Bowen ALS Prediction Prize4Life challenge. In this crowdsourcing competition, competitors developed algorithms for the prediction of disease progression of 1,822 ALS patients from standardized, anonymized phase 2/3 clinical trials. The two best algorithms outperformed a method designed by the challenge organizers as well as predictions by ALS clinicians. We estimate that using both winning algorithms in future trial designs could reduce the required number of patients by at least 20%. The DREAM-Phil Bowen ALS Prediction Prize4Life challenge also identified several potential nonstandard predictors of disease progression including uric acid, creatinine and surprisingly, blood pressure, shedding light on ALS pathobiology. This analysis reveals the potential of a crowdsourcing competition that uses clinical trial data for accelerating ALS research and development.
    Nature Biotechnology 11/2014; · 32.44 Impact Factor
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    Pouyan R. Fard, Moritz Grosse-Wentrup
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    ABSTRACT: Understanding the relationship between the decoding accuracy of a brain-computer interface (BCI) and a subject's subjective feeling of control is important for determining a lower limit on decoding accuracy for a BCI that is to be deployed outside a laboratory environment. We investigated this relationship by systematically varying the level of control in a simulated BCI task. We find that a binary decoding accuracy of 65% is required for users to report more often than not that they are feeling in control of the system. Decoding accuracies above 75%, on the other hand, added little in terms of the level of perceived control. We further find that the probability of perceived control does not only depend on the actual decoding accuracy, but is also in influenced by whether subjects successfully complete the given task in the allotted time frame.
    10/2014;
  • Moritz Grosse-Wentrup, Bernhard Schölkopf
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    ABSTRACT: Objective. Brain-computer interface (BCI) systems are often based on motor- and/or sensory processes that are known to be impaired in late stages of amyotrophic lateral sclerosis (ALS). We propose a novel BCI designed for patients in late stages of ALS that only requires high-level cognitive processes to transmit information from the user to the BCI. Approach. We trained subjects via EEG-based neurofeedback to self-regulate the amplitude of gamma-oscillations in the superior parietal cortex (SPC). We argue that parietal gamma-oscillations are likely to be associated with high-level attentional processes, thereby providing a communication channel that does not rely on the integrity of sensory- and/or motor-pathways impaired in late stages of ALS. Main results. Healthy subjects quickly learned to self-regulate gamma-power in the SPC by alternating between states of focused attention and relaxed wakefulness, resulting in an average decoding accuracy of 70.2%. One locked-in ALS patient (ALS-FRS-R score of zero) achieved an average decoding accuracy significantly above chance-level though insufficient for communication (55.8%). Significance. Self-regulation of gamma-power in the SPC is a feasible paradigm for brain-computer interfacing and may be preserved in late stages of ALS. This provides a novel approach to testing whether completely locked-in ALS patients retain the capacity for goal-directed thinking.
    Journal of Neural Engineering 08/2014; 11(5):056015. · 3.28 Impact Factor
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    ABSTRACT: Pattern recognition in neuroimaging distinguishes between two types of models: encoding- and decoding models. This distinction is based on the insight that brain state features, that are found to be relevant in an experimental paradigm, carry a different meaning in encoding-than in decoding models. In this paper, we argue that this distinction is not sufficient: Relevant features in encoding- and decoding models carry a different meaning depending on whether they represent causal-or anti-causal relations. We provide a theoretical justification for this argument and conclude that causal inference is essential for interpretation in neuroimaging.
    2014 International Workshop on Pattern Recognition in Neuroimaging (PRNI); 06/2014
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    ABSTRACT: While invasively recorded brain activity is known to provide detailed information on motor commands, it is an open question at what level of detail information about positions of body parts can be decoded from non-invasively acquired signals. In this work it is shown that index finger positions can be differentiated from non-invasive electroencephalographic (EEG) recordings in healthy human subjects. Using a leave-one-subject-out cross-validation procedure, a random forest distinguished different index finger positions on a numerical keyboard above chance-level accuracy. Among the different spectral features investigated, high β-power (20-30 Hz) over contralateral sensorimotor cortex carried most information about finger position. Thus, these findings indicate that finger position is in principle decodable from non-invasive features of brain activity that generalize across individuals.
    2014 4th International Workshop on Cognitive Information Processing (CIP); 05/2014
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    ABSTRACT: Research on the neurophysiological correlates of visuomotor integration and learning (VMIL) has largely focused on identifying learning-induced activity changes in cortical areas during motor execution. While such studies have generated valuable insights into the neural basis of VMIL, little is known about the processes that represent the current state of VMIL independently of motor execution. Here, we present empirical evidence that a subject's performance in a 3D reaching task can be predicted on a trial-to-trial basis from pre-trial electroencephalographic (EEG) data. This evidence provides novel insights into the brain states that support successful VMIL. Six healthy subjects, attached to a seven degrees-of-freedom (DoF) robot with their right arm, practiced 3D reaching movements in a virtual space, while an EEG recorded their brain's electromagnetic field. A random forest ensemble classifier was used to predict the next trial's performance, as measured by the time needed to reach the goal, from pre-trial data using a leave-one-subject-out cross-validation procedure. The learned models successfully generalized to novel subjects. An analysis of the brain regions, on which the models based their predictions, revealed areas matching prevalent motor learning models. In these brain areas, the ¿/µ frequency band (8¿14 Hz) was found to be most relevant for performance prediction. VMIL induces changes in cortical processes that extend beyond motor execution, indicating a more complex role of these processes than previously assumed. Our results further suggest that the capability of subjects to modulate their ¿/µ bandpower in brain regions associated with motor learning may be related to performance in VMIL. Accordingly, training subjects in ¿/µ-modulation, e.g., by means of a brain-computer interface (BCI), may have a beneficial impact on VMIL.
    Journal of NeuroEngineering and Rehabilitation 03/2014; 11(1):24. · 2.57 Impact Factor
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    ABSTRACT: We provide a simple method, based on volume conduction models, to quantify the neurophysiological plausibility of independent components (ICs) reconstructed from EEG/MEG data. We evaluate the method on EEG data recorded from 19 subjects and compare the results with two established procedures for judging the quality of ICs. We argue that our procedure provides a sound empirical basis for the inclusion or exclusion of ICs in the analysis of experimental data.
    Pattern Recognition in Neuro Imaging (PRNI) Workshop, 2013; 01/2013
  • Moritz Grosse-Wentrup, Bernhard Schölkopf
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    ABSTRACT: Subjects operating a brain-computer interface (BCI) based on sensorimotor rhythms exhibit large variations in performance over the course of an experimental session. Here, we show that high-frequency γ-oscillations, originating in fronto-parietal networks, predict such variations on a trial-to-trial basis. We interpret this finding as empirical support for an influence of attentional networks on BCI performance via modulation of the sensorimotor rhythm.
    Journal of Neural Engineering 06/2012; 9(4):046001. · 3.28 Impact Factor
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    ABSTRACT: Common methods of causal inference generate directed acyclic graphs (DAGs) that formalize causal relations between n variables. Given the joint distribution of all these variables, the DAG contains all information about how intervening on one variable would change the distribution of the other n-1 variables. It remains, however, a non-trivial question how to quantify the causal influence of one variable on another one. Here we propose a measure for causal strength that refers to direct effects and measure the "strength of an arrow" or a set of arrows. It is based on a hypothetical intervention that modifies the joint distribution by cutting the corresponding edge. The causal strength is then the relative entropy distance between the old and the new distribution. We discuss other measures of causal strength like the average causal effect, transfer entropy and information flow and describe their limitations. We argue that our measure is also more appropriate for time series than the known ones. Finally, we discuss conceptual problems in defining the strength of indirect effects.
    The Annals of Statistics 03/2012; · 2.53 Impact Factor
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    ABSTRACT: Despite intensive efforts, no significant benefit of rehabilitation robotics in post-stroke motor-recovery has yet been demonstrated in large-scale clinical trials. The present work is based on the premise that future advances in rehabilitation robotics require an enhanced understanding of the neural processes involved in motor learning after stroke. We present a system that combines a Barret WAM™seven degree-of-freedom robot arm with neurophysiological recordings for the purpose of studying post-stroke motor learning. We used this system to conduct a pilot study on motor learning during reaching movements with two stroke patients. Preliminary results indicate that pre-trial brain activity in ipsilesional sensorimotor areas may be a neural correlate of the current state of motor learning. These results are discussed in terms of their relevance for future rehabilitation strategies that combine rehabilitation robotics with real-time analyses of neuro-physiological recordings.
    Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on; 01/2012
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    ABSTRACT: The combination of brain-computer interfaces (BCIs) with robot-assisted physical therapy constitutes a promising approach to neurorehabilitation of patients with severe hemiparetic syndromes caused by cerebrovascular brain damage (e.g. stroke) and other neurological conditions. In such a scenario, a key aspect is how to reestablish the disrupted sensorimotor feedback loop. However, to date it is an open question how artificially closing the sensorimotor feedback loop influences the decoding performance of a BCI. In this paper, we answer this issue by studying six healthy subjects and two stroke patients. We present empirical evidence that haptic feedback, provided by a seven degrees of freedom robotic arm, facilitates online decoding of arm movement intention. The results support the feasibility of future rehabilitative treatments based on the combination of robot-assisted physical therapy with BCIs.
    Journal of Neural Engineering 06/2011; 8(3):036005. · 3.28 Impact Factor
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    ABSTRACT: A neurorehabilitation approach that combines robot-assisted active physical therapy and Brain-Computer Interfaces (BCIs) may provide an additional mileage with respect to traditional rehabilitation methods for patients with severe motor impairment due to cerebrovascular brain damage (e.g., stroke) and other neurological conditions. In this paper, we describe the design and modes of operation of a robot-based rehabilitation framework that enables artificial support of the sensorimotor feedback loop. The aim is to increase cortical plasticity by means of Hebbian-type learning rules. A BCI-based shared-control strategy is used to drive a Barret WAM 7-degree-of-freedom arm that guides a subject's arm. Experimental validation of our setup is carried out both with healthy subjects and stroke patients. We review the empirical results which we have obtained to date, and argue that they support the feasibility of future rehabilitative treatments employing this novel approach.
    IEEE ... International Conference on Rehabilitation Robotics : [proceedings]. 06/2011; 2011:5975385.
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    M. Grosse-Wentrup
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    ABSTRACT: In recent work, we have provided evidence that fronto-parietal γ-oscillations of the electromagnetic field of the brain modulate the sensorimotor-rhythm. It is unclear, however, what impact this effect may have on explaining and addressing within-subject performance variations of brain-computer interfaces (BCIs). In this paper, we provide evidence that on a group-average classification accuracies in a two-class motor-imagery paradigm differ by up to 22.2% depending on the state of fronto-parietal γ-power. As such, this effect may have a large impact on the design of future BCI-systems. We further investigate whether adapting classification procedures to the current state of γ-power improves classification accuracy, and discuss other approaches to exploiting this effect.
    Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on; 06/2011
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    ABSTRACT: This paper reviews several critical issues facing signal processing for brain-computer interfaces (BCIs) and suggests several recent approaches that should be further examined. The topics were selected based on discussions held during the 4th International BCI Meeting at a workshop organized to review and evaluate the current state of, and issues relevant to, feature extraction and translation of field potentials for BCIs. The topics presented in this paper include the relationship between electroencephalography and electrocorticography, novel features for performance prediction, time-embedded signal representations, phase information, signal non-stationarity, and unsupervised adaptation.
    Journal of Neural Engineering 03/2011; 8(2):025002. · 3.28 Impact Factor
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    Moritz Grosse-Wentrup, Donatella Mattia, Karim Oweiss
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    ABSTRACT: Analyzing neural signals and providing feedback in realtime is one of the core characteristics of a brain-computer interface (BCI). As this feature may be employed to induce neural plasticity, utilizing BCI technology for therapeutic purposes is increasingly gaining popularity in the BCI community. In this paper, we discuss the state-of-the-art of research on this topic, address the principles of and challenges in inducing neural plasticity by means of a BCI, and delineate the problems of study design and outcome evaluation arising in this context. We conclude with a list of open questions and recommendations for future research in this field.
    Journal of Neural Engineering 03/2011; 8(2):025004. · 3.28 Impact Factor
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    ABSTRACT: Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern filter (CSP) as preprocessing step before feature extraction and classification. The CSP method is a supervised algorithm and therefore needs subject-specific training data for calibration, which is very time consuming to collect. In order to reduce the amount of calibration data that is needed for a new subject, one can apply multitask (from now on called multisubject) machine learning techniques to the preprocessing phase. Here, the goal of multisubject learning is to learn a spatial filter for a new subject based on its own data and that of other subjects. This paper outlines the details of the multitask CSP algorithm and shows results on two data sets. In certain subjects a clear improvement can be seen, especially when the number of training trials is relatively low.
    Computational Intelligence and Neuroscience 01/2011; 2011:217987.
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    Moritz Grosse-Wentrup, M Correspondence, Grosse-Wentrup
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    ABSTRACT: While research on brain-computer interfacing (BCI) has seen tremendous progress in recent years, performance still varies substantially between as well as within subjects, with roughly 10 -20% of subjects being incapable of successfully operating a BCI system. In this short report, I argue that this variation in performance constitutes one of the major obstacles that impedes a successful commercialization of BCI systems. I review the current state of research on the neuro-physiological causes of performance variation in BCI, discuss recent progress and open problems, and delineate potential research programs for addressing this issue.
    International Journal of Bioelectromagnetism www.ijbem.org. 01/2011; 13.
  • Moritz Grosse-Wentrup, Bernhard Schölkopf, Jeremy Hill
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    ABSTRACT: Gamma oscillations of the electromagnetic field of the brain are known to be involved in a variety of cognitive processes, and are believed to be fundamental for information processing within the brain. While gamma oscillations have been shown to be correlated with brain rhythms at different frequencies, to date no empirical evidence has been presented that supports a causal influence of gamma oscillations on other brain rhythms. In this work, we study the relation of gamma oscillations and the sensorimotor rhythm (SMR) in healthy human subjects using electroencephalography. We first demonstrate that modulation of the SMR, induced by motor imagery of either the left or right hand, is positively correlated with the power of frontal and occipital gamma oscillations, and negatively correlated with the power of centro-parietal gamma oscillations. We then demonstrate that the most simple causal structure, capable of explaining the observed correlation of gamma oscillations and the SMR, entails a causal influence of gamma oscillations on the SMR. This finding supports the fundamental role attributed to gamma oscillations for information processing within the brain, and is of particular importance for brain-computer interfaces (BCIs). As modulation of the SMR is typically used in BCIs to infer a subject's intention, our findings entail that gamma oscillations have a causal influence on a subject's capability to utilize a BCI for means of communication.
    NeuroImage 05/2010; 56(2):837-42. · 6.25 Impact Factor
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    Alvaro Barbero, Moritz Grosse-Wentrup
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    ABSTRACT: Even though feedback is considered to play an important role in learning how to operate a brain-computer interface (BCI), to date no significant influence of feedback design on BCI-performance has been reported in literature. In this work, we adapt a standard motor-imagery BCI-paradigm to study how BCI-performance is affected by biasing the belief subjects have on their level of control over the BCI system. Our findings indicate that subjects already capable of operating a BCI are impeded by inaccurate feedback, while subjects normally performing on or close to chance level may actually benefit from an incorrect belief on their performance level. Our results imply that optimal feedback design in BCIs should take into account a subject's current skill level.
    Journal of NeuroEngineering and Rehabilitation 01/2010; 7:34. · 2.57 Impact Factor
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    ABSTRACT: Brain-Computer Interfaces (BCIs) in combination with robot-assisted physical therapy may become a valuable tool for neurorehabilitation of patients with severe hemiparetic syndromes due to cerebrovascular brain damage (stroke) and other neurological conditions. A key aspect of this approach is reestablishing the disrupted sensorimotor feedback loop, i.e., determining the intended movement using a BCI and helping a human with impaired motor function to move the arm using a robot. It has not been studied yet, however, how artificially closing the sensorimotor feedback loop affects the BCI decoding performance. In this article, we investigate this issue in six healthy subjects, and present evidence that haptic feedback facilitates the decoding of arm movement intention. The results provide evidence of the feasibility of future rehabilitative efforts combining robot-assisted physical therapy with BCIs. Moreover, the results suggest that shared-control strategies in Brain-Machine Interfaces (BMIs) may benefit from haptic feedback.
    Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Istanbul, Turkey, 10-13 October 2010; 01/2010

Publication Stats

264 Citations
72.59 Total Impact Points

Institutions

  • 2012–2014
    • Max-Planck-Institut für Intelligente Systeme, Tübingen
      • Department of Empirical Inference
      Tübingen, Baden-Württemberg, Germany
  • 2011
    • University of Tuebingen
      • Centre for Integrative Neuroscience Werner Reichardt
      Tübingen, Baden-Württemberg, Germany
    • Stanford University
      Palo Alto, California, United States
  • 2010–2011
    • Max Planck Institute for Biological Cybernetics
      Tübingen, Baden-Württemberg, Germany
  • 2009–2010
    • Universidad Autónoma de Madrid
      • Departamento de Ingeniería Informática
      Madrid, Madrid, Spain
  • 2006–2009
    • Technische Universität München
      • Institute of Automatic Control Engineering (LSR)
      München, Bavaria, Germany