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Jose del R. Millan

The University of Texas at Austin

CNBI Lab

Clinical Neuroprosthetics and Brain Interaction LabCNBI Lab

 

 
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    • Closed-Loop Brain Stimulation as a Potential Intervention for Cognitive Decline
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Hasler project

Hybrid brain-machine interfaces for natural neuroprosthetic control

Recent progress on the field of neuroprosthetics has made of them a promising assistive technology for motor substitution or  as a rehabilitation tool for people with disabilities. However, several challenges need to be overcome to allow their use in practical applications. In particular, users should be able to control them in a reliable, intuitive manner or long periods of time without requiring long and repetitive calibration periods. This project tackles these challenges through the use of shared control for hybrid BMIs for the control of upper-limb neuroprostheses, in combination with semi-supervised learning.

We will improve the accuracy and temporal precision of reach and grasp motion by combining predictions from EEG, EMG, and gaze tracking signals. New decoding methods for this hybrid BMI will increase the overall system performance allowing the prosthesis to predict accurately the patient’s intention, while leveraging the patient’s residual control. This approach will be complemented by shared-control strategies, in which the user provides high-level commands to the device, which translates them into low-level commands by means of added artificial intelligence and sensor fusion. Moreover the use of error-related brain activity and inverse reinforcement learning will provide adaptation capabilities to the system.

This project thus will advance the state of the art on shared control by incorporating advanced robot-learning approaches based on semi-supervised techniques. The new neuroprosthetic framework will be thoroughly evaluated by end-users with upper-limb motor disabilities over several days to properly asses its suitability as a key component of practical daily living assistive applications.

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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