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 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.

Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications.

In the last few decades, Brain-Computer Interface (BCI) research has focused predominantly on clinical applications, notably to enable severely disabled people to interact with the environment. However, recent studies rely mostly on the use of non-invasive electroencephalographic (EEG) devices, suggesting that BCI might be ready to be used outside laboratories. In particular, Industry 4.0 is a rapidly evolving sector that aims to restructure traditional methods by deploying digital tools and cyber-physical systems. BCI-based solutions are attracting increasing attention in this field to support industrial performance by optimizing the cognitive load of industrial operators, facilitating human-robot interactions, and make operations in critical conditions more secure. Although these advancements seem promising, numerous aspects must be considered before developing any operational solutions. Indeed, the development of novel applications outside optimal laboratory conditions raises many challenges. In the current study, we carried out a detailed literature review to investigate the main challenges and present criteria relevant to the future deployment of BCI applications for Industry 4.0.

Remapping versus short-term memory in visual stability across saccades.

Saccadic eye movements cause displacements of the image of the visual world projected on the retina. Despite the ubiquitous nature of saccades, subjective experience of the world is continuous and stable. In five experiments, we addressed the mechanisms that may support visual stability: matching of pre- and postsaccadic locations of the target by an internal copy of the saccade, or retention of the visual attributes of the target in short-term memory across the saccade. Healthy human adults were instructed to make a saccade to a peripheral Gabor patch. While the saccade was in midflight, the patch could change location, orientation, or both. The change occurred either immediately or following a 250-ms blank during which no visual stimuli were available. In separate experiments, subjects had to report either whether the patch stepped to the left or right or whether the orientation rotated clockwise or counterclockwise. Consistent with previous findings, we found that transsaccadic displacement discrimination was enhanced by the addition of the blank. However, contrary to previous findings reported in the literature, the feature change did not improve performance. Transsaccadic orientation change discrimination did not depend on either an irrelevant temporal blank or a simultaneous irrelevant target displacement. Taken together, these findings suggest that orientation is not a relevant visual feature for transsaccadic correspondence.

Modular random Boolean networks.

Random Boolean networks (RBNs) have been a popular model of genetic regulatory networks for more than four decades. However, most RBN studies have been made with random topologies, while real regulatory networks have been found to be modular. In this work, we extend classical RBNs to define modular RBNs. Statistical experiments and analytical results show that modularity has a strong effect on the properties of RBNs. In particular, modular RBNs have more attractors, and are closer to criticality when chaotic dynamics would be expected, than classical RBNs.