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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\u2019s 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\u2009\u00b1\u20090.051, with ROC-AUC of 0.971\u2009\u00b1\u20090.051 and PR-AUC of 0.934\u2009\u00b1\u20090.132. 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.<\/jats:p>","DOI":"10.1186\/s40708-026-00302-4","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T01:23:10Z","timestamp":1778462590000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A deep hybrid CNN-BiLSTM-BiGRU architecture with explainability for mild cognitive impairment detection using EEG"],"prefix":"10.1186","volume":"13","author":[{"given":"Aishik","family":"Tokdar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lakshya","family":"Agarwal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shataghnee","family":"Chatterjee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Sukriti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,11]]},"reference":[{"key":"302_CR1","doi-asserted-by":"publisher","first-page":"1966","DOI":"10.1109\/TNSRE.2020.3013429","volume":"28","author":"S Siuly","year":"2020","unstructured":"Siuly S et al (2020) A new framework for automatic detection of patients with mild cognitive impairment using resting-state EEG signals. 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