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Updated: Jul 12, 2026

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Published on: March 10, 2026

Interpretable feature-transformer framework for cross-subject MCI detection using nonlinear dynamical and

Hadi Azizpour Lindi¹, Reza Shalbaf¹, Ahmad Shalbaf²

¹Cognitive Modeling, Institute for Cognitive Science Studies, Chamran, Pardis, Tehran, 1658344575 Iran.

Cognitive Neurodynamics

|July 10, 2026

Summary

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Early detection of Mild Cognitive Impairment (MCI) is crucial. This study shows that combining EEG-derived entropy and graph features with Transformer networks achieves high accuracy (97.04%) for distinguishing MCI from healthy controls.

Area of Science:

Neuroscience
Biomedical Engineering
Artificial Intelligence

Background:

Mild Cognitive Impairment (MCI) is a precursor to Alzheimer's disease (AD), necessitating early detection for intervention.
Electroencephalography (EEG) offers a non-invasive method for assessing brain activity, with potential for MCI diagnosis.

Purpose of the Study:

To evaluate the efficacy of entropy- and graph-based EEG features for differentiating MCI from healthy controls (HC).
To compare the performance of a Transformer network against an EEGNet model when applied to engineered EEG features.

Main Methods:

Utilized resting-state EEG data from 183 participants (56 MCI, 127 HC).
Extracted nonlinear dynamical measures and graph-theoretic connectivity features across five frequency bands.

Keywords:

Alzheimer’s disease Deep learning EEG EEGNet Entropy Graph theory MCI Nonlinear dynamics Resting state SHAP Transformer

Related Experiment Videos

Last Updated: Jul 12, 2026

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Published on: March 10, 2026

Applied a Transformer network and an EEGNet model to the extracted feature set for classification.

Employed SHAP analysis for model interpretability.

Main Results:

The feature-based Transformer network achieved a classification accuracy of 97.04% ± 0.72.
The Transformer model outperformed the EEGNet baseline.
SHAP analysis identified key contributing features and EEG channels for accurate classification.

Conclusions:

Integrating handcrafted EEG features with attention-based Transformer models shows significant promise for early MCI detection.
Interpretable, feature-driven deep learning approaches can enhance diagnostic capabilities for neurodegenerative diseases.