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Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control.

  • Iturrate I Instituto de Investigación en Ingeniería de Aragón, Dpto. de Informática e Ingeniería de Sistemas, Universidad de Zaragoza, Spain.
  • Chavarriaga R Defitech Chair in Brain-Machine Interface, Center for Neuroprosthetics &Institute of Bioengineering, School of Engineering, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.
  • Montesano L Instituto de Investigación en Ingeniería de Aragón, Dpto. de Informática e Ingeniería de Sistemas, Universidad de Zaragoza, Spain.
  • Minguez J Instituto de Investigación en Ingeniería de Aragón, Dpto. de Informática e Ingeniería de Sistemas, Universidad de Zaragoza, Spain.
  • Millán Jdel R Defitech Chair in Brain-Machine Interface, Center for Neuroprosthetics &Institute of Bioengineering, School of Engineering, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.
  • 2015-09-11
Published in:
  • Scientific reports. - 2015
Adult
Brain
Brain-Computer Interfaces
Electroencephalography
Female
Humans
Learning
Male
Movement
Psychomotor Performance
Young Adult
English Brain-machine interfaces (BMI) usually decode movement parameters from cortical activity to control neuroprostheses. This requires subjects to learn to modulate their brain activity to convey all necessary information, thus imposing natural limits on the complexity of tasks that can be performed. Here we demonstrate an alternative and complementary BMI paradigm that overcomes that limitation by decoding cognitive brain signals associated with monitoring processes relevant for achieving goals. In our approach the neuroprosthesis executes actions that the subject evaluates as erroneous or correct, and exploits the brain correlates of this assessment to learn suitable motor behaviours. Results show that, after a short user's training period, this teaching BMI paradigm operated three different neuroprostheses and generalized across several targets. Our results further support that these error-related signals reflect a task-independent monitoring mechanism in the brain, making this teaching paradigm scalable. We anticipate this BMI approach to become a key component of any neuroprosthesis that mimics natural motor control as it enables continuous adaptation in the absence of explicit information about goals. Furthermore, our paradigm can seamlessly incorporate other cognitive signals and conventional neuroprosthetic approaches, invasive or non-invasive, to enlarge the range and complexity of tasks that can be accomplished.
Language
  • English
Open access status
gold
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Persistent URL
https://sonar.ch/global/documents/112057
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