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)). Our main focus is on the decoding of attention, which enables communication by means of cognitive processes through conscious response to a target stimulus, e.g., the illumination of a pushbutton.

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.


  • News


    BCI demonstration at Open Lab event

    In May, a delegation from the Department of Psychology, Justus-Liebig-University Giessen, visited the LIN to further push collaborative work already started last year. The scientific highlight was the symposium "Resources of perception, action and learning" which was very inspiring to all attendies. The BMI group exhibited a binary brain-computer interface (BCI) that enables people to respond to questions only by brain activity induced by attention. All out of 18 questions were correctly decoded by the BCI, indicating that it worked perfect with the participant while average accuracy in previous experiments was about 90%.


    Honorary symposium

    On Feb. 22, 2023, an honorary symposium was held on the occasion of the retirement of Hermann Hinrichs, who was head of the AG BMI since its inception. Among others, companions such as Thomas Münte (University of Lübeck), Ariel Schönfeld (Kliniken Schmieder Heidelberg) and Steven Hillyard (UC San Diego) presented milestones of their joint research work and told one or the other anecdote. George Mangun from UC Davis was welcomed as a surprise speaker. One of the highlights was the speech held by Hans-Jochen Heinze, head of the department, and was undoubtedly very entertaining and emotional for all involved.


    Long Night of Science

    After the Long Night of Science 2021 only took place online, interested people could finally visit the research facilities in person again in 2022. The AG BMI participated with a combination of lecture and demonstration of a Brain-Computer Interface (BCI). The BCI allowed the volunteer participant to answer “yes” and “no” to spontaneously asked questions. To do so, she only had to pay attention to an the color stimulus linked to the response (green=”yes”, red=”no”), without any muscle involvement. The answer recognized by the BCI from the electrical brain activity was fed back via loudspeaker. This impressively demonstrated to the audience how reliably communication can be realized solely by paying attention to a visual stimulus.

  • Head


    Since its foundation, the working group has been headed by Prof. Dr.-Ing. Hermann Hinrichs, who retired in February 2023. Since March 2023, the AG is headed by Dr. Christoph Reichert.

    Christoph Reichert studied computer science at the Otto von Guericke University Magdeburg from 2001 to 2007. Afterwards, he worked as a research assistant at the University Clinic for Neurology in Magdeburg. He has been employed at LIN since 2015. He completed his PhD in 2016 at the Faculty of Computer Science at Otto-von-Guericke University.

    His work at the LIN was accompanied by a close cooperation with the STIMULATE research campus, where he led the Brain-Machine Interfaces research group from 2018 to 2019. Since 2022, he is one of the postdoc representatives at LIN. His main interest is in the application of machine learning for decoding electrophysiological brain signals.

  • Members


    Dr. Christoph
    Carola Schulze+49-391-6263-
    PhD student  
    Lisa Klemm
    Pia-Lauren Link  
    Technical staff member  
    Steffi Bachmann  
    Julie Morgan  
    Franziska Tittel  
    Ardiansyah Esmondo (Msc)  
    Fabian Igor Tellez Ceja (Msc)  
    Amr Farahat (Msc)  
    Bernadette Schneider (Dr.-med.)  
    David Weizel (Msc)  


  • 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.


    Completed Projects

    Diagnostic Glove: Disease Diagnoses in daily life from wearable kinematics

    A cooperation with the University Hospital’s Department of Neurology and the IKND

    The aim of this project was 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. Using sensor data in combination with machine learning techniques, we identified typical patterns associated with natural hand movements at different ages. The dynamically variable nature of individual movements was particularly challenging for decoding. Finally, we examined hand movement data from ALS patients and a control group to characterize typical patterns of impairment specific to this disease.


    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.

  • Open Science

    Open Science

    The BMI Group supports Open Science. We mainly publish Open Access and make our research data available in repositories.


    Data sets

    Reichert, C., Klemm, L., Kalyani, A., Schreiber, S., Kühn, E. & Azanon, E. 2022. Finger kinematics of natural hand movements recorded by an exoskeleton data glove in younger and elderly persons. Universitätsbibliothek, DOI: 10.24352/UB.OVGU-2022-080,

    Reichert, C., Tellez Ceja, I. F. & Dürschmid, S. 2022. Spatial attention shifts to colored items - an EEG-based brain-computer interface. Otto von Guericke University Library, Magdeburg, Germany, 19.11.2020, DOI: 10.24352/UB.OVGU-2020-155

  • Selected Publications

    Selected Publications

    Krueger J, Krauth R, Reichert C, Perdikis S, Vogt S, Huchtemann T, Dürschmid S, Sickert A, Lamprecht J, Huremovic A, et al. 2022. Functional electrical stimulation driven by a brain–computer interface in acute and subacute stroke patients impacts beta power and long-range temporal correlation. In 2022 IEEE Workshop on Complexity in Engineering (COMPENG). IEEE.

    Reichert C, Dürschmid S, Sweeney-Reed CM, Hinrichs H. 2022. Visual spatial attention shifts decoded from the electroencephalogram enable sending of binary messages. In 2022 IEEE Workshop on Complexity in Engineering (COMPENG). IEEE.

    Reichert C, Klemm L, Mushunuri RV, Kalyani A, Schreiber S, Kuehn E, Azañón E. 2022. Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms. Sensors. 22(16):1-12.

    Reichert C, Tellez Ceja IF, Sweeney-Reed CM, Heinze H-J, Hinrichs H, Dürschmid S. 2020. Impact of Stimulus Features on the Performance of a Gaze-Independent Brain-Computer Interface Based on Covert Spatial Attention Shifts. Frontiers in Neuroscience. 14:591777.

    Will M, Peter T, Hanses M, Elkmann N, Rose G, Hinrichs H, Reichert C. 2020. A robot control platform for motor impaired people. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE. pp. 2025-2030. (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics).

    Krueger J, Reichert C, Dürschmid S, Krauth R, Vogt S, Huchtemann T, Lindquist S, Lamprecht J, Sailer M, Heinze HJ, et al. 2020. Rehabilitation nach Schlaganfall: Durch Gehirn-Computer-Schnittstelle vermittelte funktionelle Elektrostimulation. Klinische Neurophysiologie. 51(3):144-155.

    Reichert C, Dürschmid S, Hinrichs H. 2020. EEG als Steuersignal: Gehirnaktivität entschlüsseln und effizient als Kommunikationsmittel für Patienten mit motorischen Defiziten nutzen. Klinische Neurophysiologie. 51(3):161-166.

    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.

  • Third Party Funds

    Third Party Funds

    Diagnostic Glove: Disease Diagnoses in Daily Life from Wearable Kinematics
    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


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


    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 - Erste Förderperiode
    2019-2021 - Zweite Förderperiode


    BMBF Forschungscampus STIMULATE, TP BMI


  • Cooperations




  • Videos


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