Is that Real enough? EEG markers of real vs virtual objects for an enhanced AR-BCI setting

Global context and issue

AR/VR presents itself as a direct way to connect the user with digital content. However, the presentation of this content still has many flaws, especially on the visual rendering. It remains quite easy to distinguish a real object from a virtual one: the latter will appear brighter, transparent, potentially badly bound to its environment, lacking shading and light reflections. In general, the realism of virtual objects is still an important issue in AR/VR. Moreover, it is possible that the realism of a virtual object, even very close to reality, negatively impacts its perception. In the same way as the Uncanny valley (Mori’s theory that the more similar an android robot is to a human being, the more disturbing its imperfections appear to us), a virtual object whose appearance is close to that of a real object may be more disturbing than a purely virtual object. Indeed, since we cannot interact in the same way with a real object as with a virtual one, this blurred boundary could disturb the user.

The general objective of this project is to define this boundary between real and virtual in an AR environment. Using EEG, we will aim at recognizing the nature of the object considered by the user. In the longer term, this recognition could allow to overcome the evaluation paradigm of objects in AR and thus 1) to improve the rendering of virtual objects in order to bring them closer to real objects in a way that is acceptable to the user and 2) to improve the interaction of the user with the objects by detecting in advance the nature (real or virtual) of the object with which he wishes to interact.

As very limited knowledge is currently available in the field, and the project is mainly exploratory.

Etienne Peillard
Etienne Peillard
Associate Professor

My research interests include human perception issues in Virtual and Augmented Reality, spatial perception in virtual and augmented environments, and more generally, the effect of perceptual biases in mixed environments.