Multimodal Odor Perception Prediction Using Olfactory EEG and Physicochemical Features of Odorants

Odor sensory evaluation is crucial in the food, textiles, and cosmetics industries. Traditional methods rely on expert human evaluators but suffer from poor repeatability, while modern electronic noses (E-noses) effectively capture objective physicochemical characteristics of odorants but fail to account for human olfactory preferences. This study investigates the integration of human olfactory preferences encoded in electroen- cephalogram (EEG) signals, with objective physicochemical prop- erties captured by E-noses. To this end, EEG data was recorded while subjects smelled mono-molecular odorants, and vibrational spectra (VS), and fingerprint features (FPF) were generated for the same odorants. The fusion of EEG- derived features with VS and FPF enabled multimodal prediction, significantly improving performance compared to using each feature individually. To extract olfactory EEG features, we propose a novel TDA-NOSE framework—Topological Data Analysis (TDA)- based Neural Olfactory Signal Extraction (NOSE)—followed by a 1D-CNN- based classification network. This work is the first investigation of olfactory EGG analysis with TDA. TDA involves phase space reconstruction (PSR) to transform EEG time-series data into a phase space representation, followed by persistent homology to capture its topological properties. Along with olfactory perception prediction, we also show the effectiveness of TDA-NOSE features on olfactory EEG classification. TDA-NOSE features for single- channel EEG achieved 81.36% accuracy in a ten-class and 71% accuracy in binary olfactory EEG classification, significantly outperforming previous benchmarks of 13.5% and 57% achieved using all 64 EEG channels on the dataset. The findings highlight the potential of multimodal feature fusion in improving odor perception prediction and the use of TDA-based features in olfactory EEG analysis. The source codes and trained models are available at: https://github.com/durgesgameta/TDA-NOSE/