Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI) are neurodegenerative disorders that require early and accurate diagnosis for effective intervention. Electroencephalography (EEG) is a non-invasive tool for detecting cognitive decline, but subject variability poses a significant challenge in classification models. This work proposes Neurological Domain Adaptation with Transformer (NDAT), a multi-input Transformer-based framework that incorporates Instance Normalization (IN) and Adversarial Domain Adaptation (ADA) for subject-independent EEG-based classification of AD and MCI. The model extracts feature from 1D EEG signals using a Transformer encoder and from 2D EEG spectrograms using a Custom Convolutional Neural Network (Custom CNN). A fusion network aligns these multi-modal features for final classification. To mitigate subject-specific biases, Instance normalization is applied to the extracted features. Additionally, ADA is integrated using a Gradient Reversal Layer (GRL), ensuring the model learns domain-invariant representations for robust subject-independent classification. The framework is evaluated on two EEG datasets: one for Alzheimer’s disease classification (Normal, Frontotemporal Dementia (FTD), AD) and another for MCI classification (Normal, MCI, AD). To address the class imbalance in the FTD category, augmentation, and resampling techniques are applied to improve generalization. Experimental results demonstrate that NDAT significantly outperforms conventional methods, achieving high accuracy, sensitivity, and specificity in both subject-dependent and subject-independent settings. These findings highlight the effectiveness of deep learning-based feature extraction, domain adaptation, and normalization strategies in enhancing EEG-based neurodegenerative disease classification.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Nirmala Devi A and Latha M;
Methodology: Nirmala Devi A;
Visualization: Latha M;
Investigation: Nirmala Devi A and Latha M;
Supervision: Nirmala Devi A;
Validation: Latha M;
Writing- Reviewing and Editing: Nirmala Devi A and Latha M; All authors reviewed the results and approved the final version of the manuscript.
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Author(s) thanks to Dr. Latha M for this research completion and support.
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Nirmala Devi A
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamil Nadu, India.
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Cite this article
Nirmala Devi A and Latha M, “A Novel EEG Based Alzheimers Classification Framework Using Multistage Feature Fusion and Domain Adaptation”, Journal of Machine and Computing, vol.5, no.3, pp. 1477-1491, July 2025, doi: 10.53759/7669/jmc202505117.
