Entropy-Based Analysis of Olfactory EEG as a Candidate Biomarker for Early Mild Cognitive Impairment Detection: A Proof-of-Concept Study.

A decline in olfactory ability represents one of the earliest signs of Alzheimer's disease (AD) and can be valuable information for early diagnosis at the stage of mild cognitive impairment (MCI). Nevertheless, the underlying neurophysiological mechanisms of olfactory impairment have not been systematically studied and, so far, have not been applied to make objective diagnoses using electroencephalography (EEG). To fill this gap, this proof-of-concept study investigates the possibility of using olfactory-evoked EEG complexity to discriminate between healthy subjects (HSs) and MCI patients. To this purpose, a publicly available olfactory oddball EEG-recording dataset was considered. First, a strategy for cleaning the EEG signals was implemented and applied, including exclusion of participants, channels, and epochs affected by substantial artifacts and noise. Then, a dedicated preprocessing pipeline was implemented: in particular, the cleaned signals were partitioned into three temporal intervals according to the stimulus onsets (i.e., pre-stimulus, early post-stimulus, and late post-stimulus periods). For each window of interest, a novel metric-namely, the Multivariate Multiscale Multi-Frequency Entropy (M3FrEn)-was computed across 10 temporal scales. The obtained results showed significant main effects of