Dual-Stream EEG Decoding for 3D Visual Perception

Ninon Lizé Masclef, Taisija Demcenko, Antonella Catanzaro, Nataliya Kosmyna
Proceedings of the 4th (2025) and 3rd (2024) NeurIPS Workshops on Symmetry and Geometry in Neural Representations, PMLR 282:316-332, 2026.

Abstract

This paper explores a novel brain decoding model for 3D shape perception through a dual pathway architecture mirroring biological vision. Our bio-inspired approach decomposes 3D visual processing into object identity (ventral pathway) and spatial orientation (dorsal pathway) during continuous rotations. We employ circular regression for angle prediction and develop EEG-conditioned multiview diffusion for 3D reconstruction. Our approach successfully decodes both object identity and spatial orientation from EEG signals and demonstrates feasible 3D reconstruction from neural activity, with interpretability analysis revealing ventral pathway activation patterns that support object recognition performance.

Cite this Paper

BibTeX
@InProceedings{pmlr-v282-masclef26a, title = {Dual-Stream EEG Decoding for 3D Visual Perception}, author = {Masclef, Ninon Liz\'e and Demcenko, Taisija and Catanzaro, Antonella and Kosmyna, Nataliya}, booktitle = {Proceedings of the 4th (2025) and 3rd (2024) NeurIPS Workshops on Symmetry and Geometry in Neural Representations}, pages = {316--332}, year = {2026}, editor = {Acosta, Francisco and Azeglio, Simone and Tolooshams, Bahareh and van de Geijn, Chase and Shewmake, Christian and Sanborn, Sophia and Miolane, Nina}, volume = {282}, series = {Proceedings of Machine Learning Research}, month = {14 Dec 2024--07 Dec 2025}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v282/main/assets/masclef26a/masclef26a.pdf}, url = {https://proceedings.mlr.press/v282/masclef26a.html}, abstract = {This paper explores a novel brain decoding model for 3D shape perception through a dual pathway architecture mirroring biological vision. Our bio-inspired approach decomposes 3D visual processing into object identity (ventral pathway) and spatial orientation (dorsal pathway) during continuous rotations. We employ circular regression for angle prediction and develop EEG-conditioned multiview diffusion for 3D reconstruction. Our approach successfully decodes both object identity and spatial orientation from EEG signals and demonstrates feasible 3D reconstruction from neural activity, with interpretability analysis revealing ventral pathway activation patterns that support object recognition performance.} }
Endnote
%0 Conference Paper %T Dual-Stream EEG Decoding for 3D Visual Perception %A Ninon Lizé Masclef %A Taisija Demcenko %A Antonella Catanzaro %A Nataliya Kosmyna %B Proceedings of the 4th (2025) and 3rd (2024) NeurIPS Workshops on Symmetry and Geometry in Neural Representations %C Proceedings of Machine Learning Research %D 2026 %E Francisco Acosta %E Simone Azeglio %E Bahareh Tolooshams %E Chase van de Geijn %E Christian Shewmake %E Sophia Sanborn %E Nina Miolane %F pmlr-v282-masclef26a %I PMLR %P 316--332 %U https://proceedings.mlr.press/v282/masclef26a.html %V 282 %X This paper explores a novel brain decoding model for 3D shape perception through a dual pathway architecture mirroring biological vision. Our bio-inspired approach decomposes 3D visual processing into object identity (ventral pathway) and spatial orientation (dorsal pathway) during continuous rotations. We employ circular regression for angle prediction and develop EEG-conditioned multiview diffusion for 3D reconstruction. Our approach successfully decodes both object identity and spatial orientation from EEG signals and demonstrates feasible 3D reconstruction from neural activity, with interpretability analysis revealing ventral pathway activation patterns that support object recognition performance.
APA
Masclef, N.L., Demcenko, T., Catanzaro, A. & Kosmyna, N.. (2026). Dual-Stream EEG Decoding for 3D Visual Perception. Proceedings of the 4th (2025) and 3rd (2024) NeurIPS Workshops on Symmetry and Geometry in Neural Representations, in Proceedings of Machine Learning Research 282:316-332 Available from https://proceedings.mlr.press/v282/masclef26a.html.

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