Digitising Smell: From Natural Statistics of Olfactory Perceptual Space to Digital Transmission of Odors

ERC (European Research Council)HORIZON-ERC-SYGID: 101118977
EC Contribution
€118,359
Consortium Size
4 orgs
Start Year
2024
Summary

This proposal is framed by a technological goal: We aim to digitize smell. Achieving this is currently prevented by gaps in basic science. We aim to fill these gaps, culminating in a proof of concept for our model. The primary gap we identify is lack of data on what humans typically smell. Phrased conceptually, in Aim 1 we ask what are the natural statistics of human olfactory perceptual space. We address this in a series of three experiments, highlighted by one where we equip participants with a wearable sampling apparatus we designed and built for this proposal. The apparatus measures sniffing behaviour to identify odor sampling, measures neural activity to verify olfactory perception, takes video of the visual scene, analyses total levels of volatile organic compounds in real time, and collects odor samples for detailed analysis off line. In other words, we generate an olfactory equivalent of Google Street View, with the addition of chemical, perceptual and neural data. Using this we will characterise the natural statistics of human olfactory perceptual space. Moreover, a major contribution of this proposal will be in posting this massive data as a publicly available recourse. Next, in Aim 2 we will use this data to digitize human olfactory perceptual space. We build on a model that allows us to recreate odors using a restricted set of odor primaries. We will test our model in two frameworks: One we call SmelloVision, where we develop the algorithmic framework to generate an odor to match any digital image, and one we call TelleSmell, where we develop a device to sense the environment, the algorithmic framework to transfer the data, and a device to generate the corresponding odor remotely. We provide pilot data for Aim 2 where we sensed an odor in Mainz (Germany), transmitted the data over IP to Rehovot (Israel), where we successfully recreated the smell. This was, as far as we know, the first transmission of odor over IP.

Consortium (4)

Project Results (7)

Source: CORDIS, the EU research results database.

Publications (7)
Deep Learning Amplified Early Stopping Bias: Overestimating Performance on Small Datasets
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)· 2025DOI
Nona Rajabi, Antônio H. Ribeiro, Miguel Vasco, Danica Kragic
Exploring the feasibility of olfactory brain–computer interfaces
Scientific Reports· 2025DOI
Nona Rajabi, Irene Zanettin, Antônio H. Ribeiro, Miguel Vasco, Mårten Björkman, Johan N. Lundström, Danica Kragic
Methodological determinants of signal quality in electrobulbogram recordings
Journal of Neuroscience Methods· 2025DOI
Frans Nordén, Irene Zanettin, Tora Olsson, Artin Arshamian, Mikael Lundqvist, Fahimeh Darki, Johan N. Lundström
Personal care products disrupt the human oxidation field
Science Advances· 2025DOI
Nora Zannoni, Pascale S. J. Lakey, Youngbo Won, Manabu Shiraiwa, Donghyun Rim, Charles J. Weschler, Nijing Wang, Tatjana Arnoldi-Meadows, Lisa Ernle, Anywhere Tsokankunku, Gabriel Bekö, Pawel Wargocki, Jonathan Williams
Ambient air pollution undermines chemosensory sensitivity – a global perspective
Scientific Reports· 2024DOI
Anna Oleszkiewicz, Andrea Pozzer, Jonathan Williams, Thomas Hummel
Can Transformers Smell Like Humans?
Advances in Neural Information Processing Systems 37· 2024DOI
Mårten Björkman, Danica Kragic, Antônio Ribeiro, Farzaneh Taleb, Miguel Vasco
Nature and human well-being: The olfactory pathway
Science Advances· 2024DOI
Gregory N. Bratman, Cecilia Bembibre, Gretchen C. Daily, Richard L. Doty, Thomas Hummel, Lucia F. Jacobs, Peter H. Kahn, Connor Lashus, Asifa Majid, John D. Miller, Anna Oleszkiewicz, Hector Olvera-Alvarez, Valentina Parma, Anne M. Riederer, Nancy Long Sieber, Jonathan Williams, Jieling Xiao, Chia-Pin Yu, John D. Spengler