# Interpretable Feature-Transformer Framework for Cross-Subject MCI Detection Using Nonlinear Dynamical and Graph-Theoretic EEG Features

## TL;DR

This study uses EEG data and a Transformer model to detect early signs of cognitive decline, showing high accuracy and interpretability.

## Contribution

A novel interpretable feature-Transformer framework for MCI detection using nonlinear and graph-based EEG features.

## Key findings

- The feature-based Transformer achieved 97.04% accuracy in distinguishing MCI from healthy controls.
- SHAP analysis identified key nonlinear and connectivity features and EEG channels critical for classification.
- The Transformer outperformed EEGNet, showing the benefit of attention-based modeling with handcrafted features.

## Abstract

Early and accurate detection of Mild Cognitive Impairment (MCI) is essential for preventing progression toward Alzheimer’s disease (AD). In this cross-subject study, we investigate the effectiveness of entropy- and graph-based EEG features for distinguishing MCI from healthy controls (HC), using two modeling approaches: (1) a Transformer network applied to the engineered feature set, and (2) an EEGNet model trained on the same feature representation for comparison. The dataset consists of resting-state, eyes-closed EEG recordings from 183 participants (127 HC, 56 MCI), collected using a 20-channel STAT™ X24 wireless system and segmented into 3-second epochs. EEG data underwent standard preprocessing, including band-pass filtering, downsampling, normalization, and class-balancing augmentation applied to the minority class. From each channel, nonlinear dynamical measures (e.g., sample and fuzzy entropy, Higuchi fractal dimension, Lyapunov exponent) and graph-theoretic connectivity descriptors derived from coherence matrices across five frequency bands were extracted, yielding a structured 19×77 feature representation. The feature-based Transformer achieved the best performance (97.04% ± 0.72), outperforming the feature-based EEGNet baseline and highlighting the benefits of combining rich handcrafted features with attention-based modeling. SHAP (SHapley Additive exPlanations) analysis provided global and local interpretability, revealing the most influential nonlinear and connectivity features as well as the EEG channels contributing most to classification. Overall, these results demonstrate the effectiveness of feature-Transformer integration and support the potential of interpretable feature-driven deep learning models for early MCI detection.