Brain-Machine Interfaces: The Perception-Action Closed Loop: A Two-Learner System
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A brain-machine interface (BMI) is about transforming neural activity into action and sensation into perception (Figure 1). In a BMI system, neural signals recorded from the brain are fed into a decoding algorithm that translates these signals into motor outputs to control a variety of practical devices for motor-disabled people [1]-[5]. Feedback from the prosthetic device, conveyed to the user either via normal sensory pathways or directly through brain stimulation, establishes a closed control loop.
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Successful BCI requires that the user and the embedded decoder engage in a mutual learning process, in which users must learn to generate distinct brain patterns for different mental tasks, while machine learning techniques ought to discover, interpret, and allow a model's adaptation to the potentially changing individual brain patterns associated to these tasks.
“Under this view, successful BCI requires that the user and the embedded decoder engage in a mutual learning process, in which users must learn to generate distinct brain patterns for different mental tasks, while machine learning techniques ought to discover, interpret, and allow a model’s adaptation to the potentially changing individual brain patterns associated to these tasks [22].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [22]
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- Authors
- Millan, J. D. R.
- Venue
- IEEE Systems, Man, and Cybernetics Magazine
- Published
- 2015-01
- Volume
- 1
- Issue
- 1
- Pages
- 6-8
- ISSN
- 2333-942X (ISSN-L)
- OpenAlex
- W2329385490
- Cited by
- 19
- References
- 12
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 1 · 2021: 2 · 2020: 5 · 2018: 3 · 2017: 3 · 2016: 2
- FWCI
- 1.60
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Locations
- IEEE Systems Man and Cybernetics Magazine (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Keywords
- brain-computer interface, perception-action loop, feedback control, neural decoding, motor control, prosthesis, sensory feedback, motor impairment
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