AG Brain-Machine Interface

The Brain-Machine Interface (BMI) work group focuses on neurophysiological approaches to decode 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 lost 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. Michael Scholz+ 49-391-6263-92521michael.scholz@med.ovgu.de

     

  • Projects

    Projects

    Visual spatial attention as a control signal

    In this project we develop novel paradigms that permit to generate control signals, simply by directing attention to peripherally presented symbols (N2pc paradigm), independent of eye movemements. To achieve this, we decode the electroencephalogram using an efficient dedicated algorithm. In the long run, we intend to probe this procedure to provide an instrument of communication for severely paralyzed patients.

    Unidirectional Communication using collaborative BMIs

    Collaborative BMIs decode brain activity of different persons who pursue the same goal at the same time, i.e. they try to generate the same control signal using their brain activity. In this project we aim at extending this idea and utilize the synchrony and asynchrony of two simultaneously recorded electroencephalograms to transmit information from one person to another person. Our goal is to show that covert communication, i.e. unrecognizable to a third person, is possible and that this kind of communication is more reliable than common approaches in communication BMIs.

    Diagnostic Glove: Disease Diagnoses in daily life from wearable kinematics

    The aim of this project is to use data gloves (gloves or exoskeletons equipped with sensors) to detect specific motor diseases in early stages. Some motor diseases can subjectively not be differentiated in early stages but their prognoses are differently severe. We aim to uncover typical movement patterns, difficult to recognize for neurologists, using the sensor data combined with machine learning techniques. The challenge is to correctly detect the dynamically changing, individual movements and classify them according to a disease.

     

    Completed 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

    Hinrichs H, Scholz M, Baum AK, Kam JWY, Knight RT, Heinze HJ.(2020) Comparison between a wireless dry electrode EEG system with a conventional wired wet electrode EEG system for clinical applications. Sci Rep.;10(1):5218. doi: 10.1038/s41598-020-62154-0.

    C. Reichert, S. Dürschmid, M.V. Bartsch, J.M. Hopf, H.-J. Heinze, H. Hinrichs, 2020, Decoding the covert shift of spatial attention from electroencephalographic signals permits reliable control of a brain-computer interface. Journal of Neural Engineering, doi: 10.1088/1741-2552/abb692

    A. Farahat, C. Reichert, C.M. Sweeney-Reed, H. Hinrichs, 2019, Convolutional neural networks for decoding of covert attention focus and saliency maps for EEG feature visualization. Journal of Neural Engineering 16(6), doi: 10.1088/1741-2552/ab3bb4

    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.

    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

    2019-2022
    Diagnostic Glove: Disease Diagnoses in Daily Life from Wearable Kinematics
    CBBS-Neuronetzwerke
    Funding by the federal state of Saxony-Anhalt and the „European Regional Developement Fund“ (ERDF 2014-2020), Vorhaben: Center for Behavioral Brain Sciences (CBBS), FKZ: ZS/2016/04/78113
     

    2018-2020
    Medizin und Digitalisierung, Landesförderung
    (gemeinsam mit H.J. Heinze, O. Speck, E. Düzel)
     

    2016-2019
    Home – Entwicklung eines Home-Monitoring-Systems neurologsicher und anderer klinischer Parameter zur Erhaltung der Häuslichkeit, AiA-EFRE-/Landesförderung
    (gemeinsam mit H.J. Heinze)

    •     2016-2019 - First funding period
    •     2019-2021 - Second funding period
       

    Abgeschlossene Drittmittelprojekte

    2016-2019
    BMBF Forschungscampus STIMULATE, TP BMI

     

  • Cooperations

    Cooperations

    Intern

    &nbs

    Extern

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