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Ulbert | Lab

Home/NewsResearch labsMembersPublicationsDatasetsGrantsCollaborationsMediaPositions/Contact
  • Home/NewsResearch labsMembersPublicationsDatasetsGrantsCollaborationsMediaPositions/Contact
    Home/NewsResearch labsMembersPublicationsDatasetsGrantsCollaborationsMediaPositions/Contact
    Home/NewsResearch labsMembersPublicationsDatasetsGrantsCollaborationsMediaPositions/Contact
    • Human-Machine Interfaces Lab

      Development of non-invasive brain-machine interfaces and corresponding software for various human applications

      Brain-computer interfacing

      EEG, ECoG

       

      High bandwidth brain-computer interfaces (BCIs) could create a whole new world of opportunities: they could allow us to type with our inner voice, control wheelchairs, exoskeletons and robots telekinetically, etc. However, current brain-computer interfaces lack the necessary information transfer rate for these applications, so they are very inaccurate and slow compared to everyday input devices such as the keyboard, the touchpad and the mouse. In order to increase the efficiency of BCIs, our research group is developing of state of the art artificial intelligence based neural signal processor algorithms for both electroencephalographic (EEG) and electrocorticographic (ECoG) signals.

      Cybathlon 2020 (ETH Zürich)

      Cybathlon - moving people and technology

      We are eager to participate in the Cybathlon 2020 competition in Zurich, during which movement impaired users have to control a computer game with their thoughts only. The neural signals which we exploit for this competition are generated by imaginary movements.

      Video about the Cybathlon 2016.

      Eye-tracking and gesture recognition

      EMG, EOG, Eye-tracking

      Within the Hungarian National Bionic Program, we are developing a wearable eye tracker device with additional gesture recognition functionalities realized via electromyography (EMG). Our primary goal is to help disabled people in communication and allow them to control various electronic devices. Eye-tracking can also be used to identify children who will be probably affected by dyslexia, even before they start to learn how to read.

    • Members

      • Gergely Márton, PhD (Research associate, group leader)
      • Tibor Nánási, MD, PhD (Research associate)
      • Csaba Köllőd, MSc (PhD student)
      • János Rokai, MD (PhD student)
      • Ward Fadel, MSc (PhD student, Stipendium Hungaricum)
      • Moutz Wahdow, MSc (PhD student, Stipendium Hungaricum)
      • András Adolf, MSc (PhD student)
      • Melinda Rácz, MSc (PhD student)

    • Lab Facilities

      • ActiCHamp Plus 64CH EEG recording system
      • Intel RealSense depth camera
      • 3D printer: Formlabs Form2
      • 3D printer: Prusa MK3S
      • 3Dscanner: Primesense Carmine 1.09
      • Tobii 4C eye tracker
      • Emotiv Epoc+ Brain-Computer Interface headset
      • mBrainTrain Smarting 24-channel EEG recording system

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