MCI Detection from Odor-Evoked EEG Using a Multibranch Attention-Based Temporal-Spectral CNN

Dementia is a progressive neurodegenerative condition often preceded by Mild Cognitive Impairment (MCI), which is marked by early-stage memory difficulties and reduced cognitive flexibility. Detecting MCI at an early stage is crucial for timely intervention and for improving long-term cognitive health and quality of life. In this paper, we aim to differentiate between normal subjects and those suffering from MCI based on odor-evoked brain potentials from EEG signals. To address this challenge, we used publicly available multichannel EEG data and calculated a set of temporal-spectral components using wavelets, spectral grouping, and canonical correlation. These features are fed separately into attention-based convolutional neural network (CNN) models, which are individually trained on each feature-set, leading to individual feature branches. Later, these branches are fed into a fully connected network for performing the classification task. Our experiments demonstrate that the proposed method outperforms other methods considered in this paper. Ablation studies also reveal the individual strength of each set of features adopted in this study, along with their combined strength when the entire feature set is used for classification.