Multimodal EEG-Based Classification of Alzheimer's and MCI Using Olfactory Event-Related Potentials and Transformers

Neurodegenerative diseases such as Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI) are characterized by insidious cognitive decline, often preceded by olfactory dysfunction. Emerging evidence from cognitive neuroscience and olfaction research suggests that odor-evoked brain responses may serve as sensitive biomarkers for early neurodegenerative changes. This study proposes a multimodal framework integrating cognitive event-related potentials (ERPs), olfactory stimulus processing, and machine learning-based disease classification to detect early signs of MCI and AD using electroencephalography (EEG).We utilize a publicly available EEG dataset recorded during olfactory oddball paradigms to investigate differential neural responses to standard versus deviant odors across three cohorts: healthy controls, MCI patients, and individuals with AD. First, electrophysiological signatures such as the P300 and N200 components are analyzed to characterize cognitive processing of olfactory stimuli. Second, time-frequency analyses and source localization methods are employed to delineate latency, amplitude, and cortical activation differences in response to olfactory deviance. Third, engineered EEG features, including ERP peak amplitudes and spectral power in alpha, beta, and gamma bands, are used to train deep learning models, particularly Transformer architectures, for robust multi-class classification.Preliminary findings indicate significant group-level differences in ERP profiles and classification metrics, demonstrating the diagnostic potential of olfactory EEG responses. The proposed approach offers a non-invasive, cost-effective adjunct for early detection of neurodegeneration, advancing the intersection of olfactory neuroscience, cognitive electrophysiology, and clinical neuroinformatics.