Olfactory ERP-based classification of anosmia and normosmia using machine learning.

Objective: This study aimed to investigate whether electroencephalogram (EEG) data can be used to classify normosmia and anosmia using machine learning approaches and to examine how classification performance varies across single and combined chemical stimulation conditions. Methods: EEG signals were recorded from 66 participants (26 normosmic and 40 anosmic) during olfactory stimulation with phenyl ethyl alcohol (PEA), hydrogen sulfide (H₂S), and carbon dioxide (CO₂), presented to the left and right nostrils. EEG data were averaged for each condition, resulting in six chemosensory event-related potential (CSERP) datasets per participant. Three machine learning models-support vector machine (SVM), random forest (RF), and convolutional neural network (CNN)-were used to classify normosmia and anosmia. Results: Among all single and combined odor conditions, the combination of PEA_Right + H₂S_Left + H₂S_Right achieved the highest performance (ACC = 86.37%, F1 = 0.88, AUC = 0.85). CNN generally outperformed SVM and RF across most conditions. H₂S stimulation consistently provided strong discriminative power, particularly under left-sided presentation. Conclusion: Machine learning applied to EEG-based CSERPs can effectively distinguish olfactory dysfunction, demonstrating the feasibility of this approach for objective evaluation of olfactory processing. Significance: These findings suggest that EEG-based machine learning can serve as an objective framework for assessing olfactory function and may contribute to future development of data-driven diagnostic strategies for smell disorders.