Brain Machine Interface

The Brain Machine Interface (BMI) work group focuses on neurophysiological approaches to decode intended movements or other actions from human brain activity (in particular as reflected in the electroencephalogram (EEG).

Patients after stroke or paraplegia often suffer from motor or sensory dysfunction. The goal of the BMI projects is to support these patients in regaining basic motor or sensory function by means of brain controlled devices.

Besides the EEG our research includes experiments based on brain magnetic fields (magnetoencephalogram (MEG) and functional magnetic resonance imaging (fMRI). The analysis of these signals is widely based on machine learning approaches.

 

  • Head

    Head

    Hermann Hinrichs studied physics at the University of Hanover from 1966 to 1972. There he received his doctorate in 1982 at the Institute for Theoretical Communications Engineering and Information Processing. In 1991 he acquired the venia legendi for Experimental Neurophysiology at the Hannover Medical School. In 1998 he was appointed an extraordinary professor. In addition to his work at LIN, he is responsible for research-related technology and methodology at the Department of Neurology of the OVGU. He is also a member of the board of directors of the research campus STIMULATE. His scientific interest is in the measurement and analysis of neurophysiological signals.

     

  • Members

    Members

    Head  
    Prof. Dr. Hermann Hinrichs+49-391-6263-92051hermann.hinrichs@med.ovgu.de
    Sekretary  
    Carola Schulze+49-391-6263- 92311carola.schulze@med.ovgu.de
    Postdocs  
    Dr. Christoph Reichert+49-391-6263-92531christoph.reichert@lin-magdeburg.de
    Dr. Stefan Dürschmid+49-391-6263-92531stefan.duerschmid@med.ovgu.de
    PhD students  
    Gennady Sintotskiy  

     

  • Projects

    Projects

    Brain-Machine Interface – OP planning and selection procedures (as subproject of the research campus STIMULATE)

    The project is concerned with the investigation of paradigms and algorithms that make it possible to identify brain function for robust BMI control in different forms of brain lesion after stroke. On the one hand, this includes the individual optimal localization of non-invasive or invasive electrodes (grids) and, on the other hand, enables the detection of intended actions in the single triangle, i.e. in real-time, and the generation of machine commands. Therefore, in this work package, selection procedures are to be developed which allow a simple intuitive control of an autonomously acting robot arm.



    In-Ear-BMI (as subproject of the research campus STIMULATE)

    This project comprises the development and implementation of hardware and software components for a miniaturized system for the acquisition of EEG signals from the auditory canal, realized as a smartphone extension. Research goals include the development of suitable electrodes and electrode localizations, dedicated miniaturized amplifiers/signal converters and smartphone-based analysis software that can extract specific brain function parameters from the EEG based on neuroscientific approaches.

     

  • Selected Publications

    Selected Publications

    Reichert C, Heinze N, Pfeiffer T, Dürschmid S, Hinrichs H. 2018. P63. Detection of error potentials from EEG and MEG recordings and its value for BMI control. Clinical Neurophysiology. 129(8):e93. Erhältlich bei: 10.1016/j.clinph.2018.04.698

    A. Farahat, C. Reichert, C. M. Sweeney-Reed, H. Hinrichs, 2018. Convolutional neural networks for EEG decoding and exploration of brain dynamics. Bernstein Conference 2018, Berlin. doi: 10.12751/nncn.bc2018.0092

    C. Reichert, S. Dürschmid, H. Hinrichs, H.-J. Heinze, C. M. Sweeney-Reed, 2018. BOLD signal is more reliable than sensorimotor EEG signals in decoding hand movements. Program No. 225.27. 2018 Neuroscience Meeting Planner. San Diego, CA: Society for Neuroscience, 2018. Online.

    S. Dürschmid, C. Reichert, H. Hinrichs, H.-J. Heinze, H.E. Kirsch, R.T. Knight, L.Y. Deouell, 2018. Direct Evidence for Prediction Signals in Frontal Cortex Independent of Prediction Error. Cerebral Cortex, bhy331. doi: 10.1093/cercor/bhy331

    Rosenow F, Audebert H J, Hamer  H M., Hinrichs H, Keler-Uberti S, Kluge T, Noachtar S, Remi J, Sotoodeh A, Strzelczyk A, Weber JE, Zöllner JP (2018). Tele-EEG: Aktuelle Anwendungen, Hindernisse und technische Lösungen, Klinische Neurophysiologie. 49, 4, S. 208-215.

    Galazky I, Kluge C, Schmitt FC, Kopitzki K, Zaehle T, Voges J, Büntjen L, Kupsch A, Hinrichs H (2018) Pallidal Stimulation Modulates Pedunculopontine Nuclei in Parkinson’s Disease. Brain Sci. 2018 Jun 25;8(7). pii: E117. doi: 10.3390/brainsci8070117.

    Sweeney-Reed CM, Zaehle T, Voges J, Schmitt FC, Buentjen L, Borchardt V, Walter M, Hinrichs H, Heinze HJ, Rugg MD, Knight RT (2017) Anterior Thalamic High Frequency Band Activity Is Coupled with Theta Oscillations at Rest. Front Hum Neurosci.;11:358

    Kam JWY, Griffin S, Shen A, Patel S, Hinrichs H, Heinze HJ, Deouell LY, Knight RT (2019). Systematic comparison between a wireless EEG system with dry electrodes and a wired EEG system with wet electrodes. Neuroimage. 2019 Jan 1;184:119-129. doi: 10.1016/j.

  • Current Third Party Funds

    Current Third Party Funds

    2016-2019
    BMBF Research Campus STIMULATE, TP BMI
    https://www.forschungscampus-stimulate.de

     

    2018-2020
    Medicine and digitisation, state funding
    (together with H.J. Heinze, O. Speck, E. Düzel)
    http://www.med.uni-magdeburg.de/kneu/en/Research/Research+Groups/Workgroups/Medicine+and+Digitalization.html

     

    2016-2019
    Development of a home monitoring system for neurologsicher and other clinical parameters for the preservation of domesticity, AiA-EFRE-/Landesförderung
    (together with H.J. Heinze)
    http://www.kneu.ovgu.de/Forschung/Förderung+_+Drittmittelprojekte/Autonomie+im+Alter.html

  • Cooperations

    Cooperations

    Intern

    &nbs

    Extern

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