Theoretical Perspective on an Ideomotor Brain-Computer Interface: Toward a Naturalistic and Non-invasive Brain-Computer Interface Paradigm Based on Action-Effect Representation

Recent years have been marked by the fulgurant expansion of non-invasive Brain-Computer Interface (BCI) devices and applications in various contexts (medical, industrial etc.). This technology allows agents “to directly act with thoughts,” bypassing the peripheral motor system. Interestingly, it is...

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Published in:Frontiers in human neuroscience Vol. 15; p. 732764
Main Authors: Le Bars, Solène, Chokron, Sylvie, Balp, Rodrigo, Douibi, Khalida, Waszak, Florian
Format: Journal Article
Language:English
Published: Lausanne Frontiers Research Foundation 28.10.2021
Frontiers
Frontiers Media S.A
Subjects:
action-effect prediction
Artificial intelligence
Brain
Cognitive science
Computer applications
Human influences
Human Neuroscience
human voluntary action
ideomotor
Implants
intention decoding
Interfaces
Medical imaging
non-invasive brain-computer interface
Paradigms
ISSN:1662-5161, 1662-5161
Online Access:Get full text
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Summary:Recent years have been marked by the fulgurant expansion of non-invasive Brain-Computer Interface (BCI) devices and applications in various contexts (medical, industrial etc.). This technology allows agents “to directly act with thoughts,” bypassing the peripheral motor system. Interestingly, it is worth noting that typical non-invasive BCI paradigms remain distant from neuroscientific models of human voluntary action. Notably, bidirectional links between action and perception are constantly ignored in BCI experiments. In the current perspective article, we proposed an innovative BCI paradigm that is directly inspired by the ideomotor principle, which postulates that voluntary actions are driven by the anticipated representation of forthcoming perceptual effects. We believe that (1) adapting BCI paradigms could allow simple action-effect bindings and consequently action-effect predictions and (2) using neural underpinnings of those action-effect predictions as features of interest in AI methods, could lead to more accurate and naturalistic BCI-mediated actions.
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Reviewed by: Deepak Kapgate, Rashtrasant Tukadoji Maharaj Nagpur University, India; Reinhold Scherer, University of Essex, United Kingdom; Joseph Thachil Francis, University of Houston, United States
This article was submitted to Brain-Computer Interfaces, a section of the journal Frontiers in Human Neuroscience
Edited by: Liana Fattore, National Research Council (CNR), Italy
ISSN:1662-5161
1662-5161
DOI:10.3389/fnhum.2021.732764