Home › Research › Mild cognitive impairment
A deep hybrid CNN-BiLSTM-BiGRU architecture with explainability for mild cognitive impairment detection using EEG
Aishik Tokdar, Lakshya Agarwal, Shataghnee Chatterjee, Sukriti. Brain Informatics, 2026-05-11
Topic: Risk, prevention and care in population studies
Accurate detection of Mild Cognitive Impairment (MCI) is critical for timely intervention and for slowing progression to Alzheimer's disease. Electroencephalography (EEG) offers a non-invasive and cost-effective measure of brain activity; however, its complex, non-linear dynamics limit conventional analysis. We propose a CNN-Res-SE-BiLSTM-BiGRU framework for the automated detection of MCI directly from raw EEG. Convolutional and residual blocks capture local temporal structure, bidirectional recurrent layers model long-range dependencies, and Squeeze-and-Excitation (SE) modules provide channel-wise attention. Predicted probabilities are calibrated using temperature scaling, and operating thresholds are selected on the validation set using Youden's J statistic. The model is evaluated using five-fold cross-validation under both subject-dependent and strict subject-independent protocols on a primary resting-state dataset, with additional subject-independent validation on an odor EEG dataset. Under subject-independent evaluation on the odor dataset, the proposed model achieved an accuracy of 0.956 ± 0.051, with ROC-AUC of 0.971 ± 0.051 and PR-AUC of 0.934 ± 0.132. UMAP-based visualization and explainable AI analyses (SHAP and LIME) provide interpretable insight into the learned spatiotemporal patterns and sample-specific decisions. These results demonstrate robust, interpretable EEG-based MCI detection with potential clinical utility.
Key findings
- These results demonstrate robust, interpretable EEG-based MCI detection with potential clinical utility.
Why it matters
This study developed a deep learning framework using EEG that detected MCI with high accuracy under subject-independent evaluation. The work is about developing an automated diagnostic tool and does not test any treatment or prevention for Alzheimer's disease.
Study details
- Evidence strengthLow
How this study scored 35/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 34 of the 96 points we could judge it on, which is 35 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Brain Informatics) |
| Journal's citation record | 0 / 4 | Brain Informatics: journal h-index 49 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 3 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.5 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | 4 / 6 | Cited 13.3× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 3 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | OpenAlex's profile for last-listed author Sukriti holds only 5 work(s), too few to judge a track record — left out of this score |
| Institution's track record | 0 / 3 | Vellore Institute of Technology University: institution h-index 351 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 42108320) |
| Free to read | 2 / 2 | Free to read |
| Points | 34 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 34 of the 96 points we could judge it on is 35 out of 100. |
| Total | 35 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1186/s40708-026-00302-4
- PubMed: 42108320
- Original source →
- Free full text: Brain Informatics →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Tokdar, A., Agarwal, L., Chatterjee, S., & Sukriti. (2026). A deep hybrid CNN-BiLSTM-BiGRU architecture with explainability for mild cognitive impairment detection using EEG. Brain Informatics. https://doi.org/10.1186/s40708-026-00302-4
Vancouver
Tokdar A, Agarwal L, Chatterjee S, Sukriti. A deep hybrid CNN-BiLSTM-BiGRU architecture with explainability for mild cognitive impairment detection using EEG. Brain Informatics. 2026. doi:10.1186/s40708-026-00302-4. PMID: 42108320.
BibTeX
@article{tokdar2026-42108320,
author = {Tokdar, Aishik and Agarwal, Lakshya and Chatterjee, Shataghnee and Sukriti},
title = {{A deep hybrid CNN-BiLSTM-BiGRU architecture with explainability for mild cognitive impairment detection using EEG}},
journal = {Brain Informatics},
year = {2026},
doi = {10.1186/s40708-026-00302-4},
pmid = {42108320},
url = {https://doi.org/10.1186/s40708-026-00302-4}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.