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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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BID

Brain-Coupled Interactive Devices

Despite important advances on computing systems, humans outperform artificial systems in tasks such as image analysis and visual recognition. However, human processing cannot cope with the increasing amount of multimodal data that is generated nowadays, requiring the use of automatic processing systems. In this project we propose the design of “Brain-coupled Interactive devices” that will combine machine learning techniques with the human cognitive ability to perform such tasks.

These devices will use brain activity –as measured by scalp electroencephalography (EEG)– to extract information about human cognitive processes such as visual processing, pattern matching or error detection. This approach contrasts with traditional research on Brain-Computer Interfaces (BCI) that attempt to translate human intentions into control signals based on the recognition of voluntary modulation of brain rhythms.

The design of these interactive devices require advanced processing techniques that allows for real time recognition of brain electrical activity in single-trials. To this end, and based on existing techniques aimed at the estimation of the internal neural sources of scalp EEG potentials, we will develop classification techniques that not only achieve localization of neural sources, but also maximize the discriminability of EEG signals corresponging to different experimental conditions (e.g., related to different cognitive processes). This approach may allow for the design of reliable BCI systems and, at the same time, provide localization of the neural structures involved in human cognitive processes. These information can, in turn, be used to assess consistency with previous neurophysiological and imaging studies.

The use of these techniques for the recognition of human cognitive processes allows the “Brain-Coupled Interactive Devices” to successfully involve the human user into a closed-loop that aimed at the efficient processing of multimodal, machine generated information.

Keywords: electroencephalography, human-machine interaction, signal processing, machine learning.

Publications

Journal Publication

Ušćumlić M., Chavarriaga R., Millán J.d.R. (2013). An iterative framework for EEG-based image search: Robust retrieval with weak classifiers. PLOS One, 8(8):e72018.

Conference Papers

Goel M.K., Chavarriaga R., Millán J.d.R. (2017). Inverse solutions for brain-computer interfaces: Effects of regularisation on localisation and classification. IEEE SMC. Banff, Canada.

Goel M.K., Chavarriaga R., Millán J.d.R. (2014). Comparing BCI performance using scalp EEG- and inverse solution-based features. 6th Brain-Computer Interface Conf. 2014. Graz, Austria.

Ušćumlić, Chavarriaga R., Millán J.d.R. (2013). Iterative EEG-based natural image search under RSVP. 5th Int. BCI Meeting. Asilomar, USA.

Goel M., Chavarriaga R., Millán J.d.R. (2011). Cortical vs surface EEG for event related potentials-based brain-computer interfaces. 5th Int. IEEE EMBS Conf. Neural Engineering. Cancún, México.

Latest News

  • 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
  • Akhil Surapaneni: Advancing Brain Tumor Treatments

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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