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Clinical Neuroprosthetics and Brain Interaction LabCNBI Lab

 

 

Research

The overarching objective of CNBI’s research is to bring brain-machine interfaces (BMI) out of the laboratory into the daily life of people, in particular those with severe disabilities. To do so, we tackle three major scientific challenges:

  • deploying BMI systems in the clinical realm to promote neural plasticity and functional recovery of patients;
  • designing novel BMI paradigms to augment interaction experience in our daily activities;
  • developing new principles to facilitate user’s acquisition of BMI skills.

We also believe that these three aims can only be probed, advanced and demonstrated by engaging BMI users in long-term use of complex brain-controlled devices. In our quest, we depart from a dominant view in the BMI field, where the usual question researchers ask is “how to improve decoding of user’s intention?”. Although this aspect is certainly central and we don’t neglect it, we rather ask “how to facilitate subject’s acquisition of BMI skills?”

 

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Latest News

  • A Jolt of Innovation for Brain-Computer Interfaces
  • Satyam Kumar Successfully Defended his PhD Thesis. Congratulations!!
  • Hussein Alawieh Successfully Defended his PhD Thesis. Congratulations!!
  • Minsu Zhang recipient of the 2024 KSEA-KUSCO Graduate Scholarship
  • Nature Medicine: The Future of Brain–computer Interfaces in Medicine

Latest Publications

Alawieh H, Liu D, Madera J, Kumar S, Racz FS, Fey AM, Del R Millán J. Electrical Spinal Cord Stimulation Promotes Focal Sensorimotor Activation that Accelerates Brain-computer Interface Skill Learning. Proc Natl Acad Sci U S A. 2025 Jun 17;122(24):e2418920122.

Racz FS, Kumar S, Kaposzta Z, Alawieh H, Liu DH, Liu R, Czoch A, Mukli P, Millán JDR. Combining Detrended Cross-Correlation Analysis with Riemannian Geometry-based Classification for Improved Brain-computer Interface Performance. Front Neurosci. 2024 Mar 14;18:1271831.

Kumar S, Alawieh H, Racz FS, Fakhreddine R, Millán JDR. Transfer Learning Promotes Acquisition of Individual BCI Skills.PNAS Nexus, Volume 3, Issue 2, February 2024, page076.

Iwane F, Billard A, Millán JDR. Inferring Individual Evaluation Criteria for Reaching Trajectories with Obstacle Avoidance from EEG Signals. Sci Rep. 2023 Nov 17;13(1):20163.

S. Kumar S. Kumar, D. H. Liu, F. S. Racz, M. Retana, S. Sharma, F. Iwane, B. P. Murphy, R. O’Keeffe, S. F. Atashzar, F. Alambeigi, J. del R. Millán. CogniDaVinci: Towards Estimating Mental Workload Modulated by Visual Delays During Telerobotic Surgery – An EEG-based Analysis. 2023 IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, 2023, pp. 6789-6794.


For a complete list of publications go to our Publications page!

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