Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control
Open accessAbstract
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.
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Because of reinforcement learning, the BMI system can potentially store single to several control policies, allowing patient personalization.
“Because of this “reinforcement learning,” the system can potentially store single to several control “policies,” which allow for patient personalization [48].”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [48]
Recent advances in brain machine interfaces have shown that a system can store and learn a subject's intended voluntary goal-directed wishes (EEG) when the user trains an intelligent controller.
“The recent advances in brain machine interfaces (BMIs) have shown that a system can be employed where the subjects’ intended and voluntary goal-directed wishes (electroencephalogram, EEG) can be stored and learned when a user “trains” an intelligent controller (an AI).”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [48]
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Research works printed but not linked: 1
- Iturrate I., Montesano L., Chavarriaga R., Millán J. d. R. & Minguez J. Spatiotemporal filtering for EEG error related potentials. Proc. 5th Int Brain-Computer Interface Conf., Graz (Austria), 12– 15. Graz: Graz: Verlag der TU Graz (2011, September).
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- Authors
- Iturrate, I., Chavarriaga, R., Montesano, L., Minguez, J., & Millán, J. D. R.
- Affiliations
- Universidad de Zaragoza (ES)
- École Polytechnique Fédérale de Lausanne (CH)
- Corresponding author
- Millán, José del R.
- Venue
- Scientific Reports
- Published
- 2015-09-10
- Volume
- 5
- Issue
- 1
- Pages
- 13893-13893
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC4564803
- OpenAlex
- W2109065101
- Cited by
- 149
- References
- 42
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 15 · 2023: 14 · 2022: 11 · 2021: 12 · 2020: 30 · 2019: 15 · 2018: 26 · 2017: 10 · 2016: 7 · 2015: 1
- FWCI
- 8.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Zaguan (University of Zaragoza Repository) (submittedVersion, cc-by)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Keywords
- brain-computer interface, neural prostheses, cognitive monitoring, motor learning, adaptive control
- MeSH terms
- Adult, Brain, Electroencephalography, Female, Humans, Learning, Male, Movement, Psychomotor Performance, Young Adult, Brain-Computer Interfaces
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