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Jose del R. Millan

The University of Texas at Austin

CNBI Lab

Clinical Neuroprosthetics and Brain Interaction LabCNBI Lab

 

 
  • About
  • Team
    • CNBI Lab Members
    • Alumni
  • Research
  • Publications
  • Projects
    • Closed-Loop Brain Stimulation as a Potential Intervention for Cognitive Decline
    • Sinergia
    • Hasler project
    • NCCR Robotics
    • CNBI-NISSAN
    • BNCI Horizon 2020
    • TOBI
    • CNBI-Colombia
    • Opportunity
    • BID
    • BACS
    • MAIA
    • IM2, BMI Integrated Project (Swiss NCCR)
  • Events
  • News
  • Video-Media

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?”

 

Latest News

  • Akhil Surapaneni’s poster wins the Larry Abraham Excellence in Rehabilitation Research Award
  • Brain-Powered Wheelchair Shows Real-World Promise
  • UT Researchers Develop Electrode to Rehabilitate Stroke Patients at Home
  • Stable Electrodes for Long-Term, Wearable Brain-Machine Interface
  • José Del R. Millán Nominated as a Dell Medical School Visionary

Latest Journal Publications

Beraldo, G., Tonin, L., Cesta, A., Menegatti, E., Millán, J.d.R. (2023). Validation of Shared Intelligence Approach for Teleoperating Telepresence Robots Through Inaccurate Interfaces. In: Petrovic, I., Menegatti, E., Marković, I. (eds) Intelligent Autonomous Systems 17. IAS 2022. Lecture Notes in Networks and Systems, vol 577. Springer, Cham.

Luca Tonin, Serafeim Perdikis, Taylan Deniz Kuzu, Jorge Pardo, Bastien Orset, Kyuhwa Lee, Mirko Aach, Thomas Armin Schildhauer, Ramón Martínez-Olivera, José del R. Millán. Learning to control a BMI-driven wheelchair for people with severe tetraplegia. iScience. 2022. 105418.

Ju-Chun Hsieh, Hussein Alawieh, Yang Li, Fumiaki Iwane, Linran Zhao, […]. A highly stable electrode with low electrode-skin impedance for wearable brain-computer interface. Biosensors and Bioelectronics. 2022, 114756.

JH. Jeong, JH. Cho, YE. Lee, SH. Lee, GH. Shin, YS. Kweon,  J.d.R. Millán, KR. Müller, SW Lee. 2020 International brain-computer interface competition: A review. Front Hum Neurosci. 2022 Jul, 22(16):898300.

F. Dell’Agnola, P.-K. Jao, R. Chavarriaga, J.d.R. Millán, D. Floreano, D. Atienza. (2022). Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions with Drones. in IEEE J. of Biomedical and Health Informatics,2022 Sep, 26(9):4751-4762.


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

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