A SYSTEMATIC STUDY OF ROLE OF EEG MENTAL IMAGERY IN DETECTING DEMENTIA
AUTHORS:
Mentor
Dr. Shailja Shukla
Affiliation
The Bhopal School of Social Science (BSSS) , (SAGE) University, Bhopal, Sanjeev Agrawal Global Educational
DOI: 
SHARE THIS ARTICLE
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
DOWNLOAD ARTICLE
REVIEW REPORT
CITE THIS ARTICLE
Abstract
The Electroencephalography also known as EEG has emerged as a critical means in neurological valuations, particularly in the exposure of dementia-related diseases. EEG-based mental imagery has gained attention for the purpose of diagnosing disease progression monitoring. The paper aims to reconnaissance the claim in the field of Convolutional Neural Networks called CNNs as the technique in detecting dementia from Electroencephalogram (EEG) images. Dementia is a very progressive disorder of neurological disease, categorization done by the advanced deterioration of cognitive function, can be stimulating and very helpful to detect the disorder at initial phases. It explores the area to work of Convolutional Neural Networks in the detection of dementia by analyzing Electroencephalogram (EEG) images. EEG is a non-invasive method or technique which records electrical action that happens in the brain and show probability in detecting neurodegenerative diseases. The practice of deep learning work for the tactics, predominantly CNNs, offers probability in powering, automating and enhancing the research to detect correctness or the truth of dementia disease from EEG signals.
Keywords
Article Information
PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume Volume 10, Issue 01
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Feb 23 2026
Article Metrics
Article Views
13
PDF Downloads
0
HOW TO CITE
Copy
Tripathi, H. P. ,. (2026). A Systematic Study of Role of EEG Mental Imagery in Detecting Dementia. International Journal of Science, Strategic Management and Technology, Volume 10(01). https://doi.org/10.55041/ijsmt.v2i2.131
Tripathi, Habi. "A Systematic Study of Role of EEG Mental Imagery in Detecting Dementia." International Journal of Science, Strategic Management and Technology, vol. Volume 10, no. 01, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i2.131.
Tripathi, Habi. "A Systematic Study of Role of EEG Mental Imagery in Detecting Dementia." International Journal of Science, Strategic Management and Technology Volume 10, no. 01 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i2.131.
References
1.Kim, M. J., Youn, Y. C., & Paik, J. (2023). Deep learning-based EEG analysis to classify normal, mild cognitive impairment, and dementia: Algorithms and dataset. NeuroImage, 272, 120054.
2.Sánchez-Reyes, L. M., Rodríguez-Reséndiz, J., Avecilla-Ramírez, G. N., García-Gomar, M. L., & Robles-Ocampo, J. B. (2021). Impact of eeg parameters detecting dementia diseases: A systematic review. IEEE Access, 9, 78060-78074
Leracitano, C., Mammone, N., Hussain, A., & Morabito, F. C. (2020). A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia. Neural Networks, 123, 176-190.
3.Bi, X., & Wang, H. (2019). Early Alzheimer’s disease diagnosis based on EEG spectral images using deep learning. Neural Networks, 114, 119-135.
4.Bharati, P. & Pramanik, A. Deep learning techniques—R-CNN to mask R-CNN: Asurvey. Comput. Intel. Pattern Recogn. Proc. CIPR 2020, 657–668 (2019)
5.The Alzheimer’s Disease Neuroimaging Initiative (ADNI). https://adni.loni.usc.edu/ (2023).
Stam, C. J., Jelles, B., Achtereekte, H. A. M., Rombouts, S. A. R. B., Slaets, J. P. J., & Keunen, R. W. M. (1995). Investigation of EEG non-linearity in dementia and Parkinson's disease. Electroencephalography and clinical neurophysiology, 95(5), 309-317.
6.Al-Qazzaz, N. K., Ali, S. H. B. M., Ahmad, S. A., Chellappan, K., Islam, M. S., & Escudero, J. (2014). Role of EEG as biomarker in the early detection and classification of dementia. The Scientific World Journal, 2014(1), 906038.
Mehmood A, Yang S, Feng Z, Wang M, Ahmad AS, Khan R, et al. A transfer learning approach for early diagnosis of Alzheimer’s disease on MRI images. Neuroscience. 2021;460:43–2. https://doi.org/10.1016/j.neuroscience.2021.01.002.
7.https://www.springernature.com/gp/open-research/about/the-fundamentals-of-open-access-and-open-research
Murugan, S., Venkatesan, C., Sumithra, M. G., Gao, X. Z., Elakkiya, B., Akila, M., & Manoharan, S. (2021). DEMNET: A deep learning model for early diagnosis of Alzheimer diseases and dementia from MR images. Ieee Access, 9, 90319-90329.
8.Dauwels, J., Vialatte, F., & Cichocki, A. (2010). Diagnosis of Alzheimer's disease from EEG signals: where are we standing?. Current Alzheimer Research, 7(6), 487-505.
9.Babiloni, C., Del Percio, C., Lizio, R., Noce, G., Cordone, S., Lopez, S., ... & Bonanni, L. (2017). Abnormalities of cortical neural synchronization mechanisms in patients with dementia due to Alzheimer's and Lewy body diseases: an EEG study. Neurobiology of aging, 55, 143-158.
10.Henderson, G., Ifeachor, E., Hudson, N., Goh, C., Outram, N., Wimalaratna, S., ... & Vecchio, F. (2006). Development and assessment of methods for detecting dementia using the human electroencephalogram. IEEE Transactions on Biomedical Engineering, 53(8), 1557-1568.
2.Sánchez-Reyes, L. M., Rodríguez-Reséndiz, J., Avecilla-Ramírez, G. N., García-Gomar, M. L., & Robles-Ocampo, J. B. (2021). Impact of eeg parameters detecting dementia diseases: A systematic review. IEEE Access, 9, 78060-78074
Leracitano, C., Mammone, N., Hussain, A., & Morabito, F. C. (2020). A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia. Neural Networks, 123, 176-190.
3.Bi, X., & Wang, H. (2019). Early Alzheimer’s disease diagnosis based on EEG spectral images using deep learning. Neural Networks, 114, 119-135.
4.Bharati, P. & Pramanik, A. Deep learning techniques—R-CNN to mask R-CNN: Asurvey. Comput. Intel. Pattern Recogn. Proc. CIPR 2020, 657–668 (2019)
5.The Alzheimer’s Disease Neuroimaging Initiative (ADNI). https://adni.loni.usc.edu/ (2023).
Stam, C. J., Jelles, B., Achtereekte, H. A. M., Rombouts, S. A. R. B., Slaets, J. P. J., & Keunen, R. W. M. (1995). Investigation of EEG non-linearity in dementia and Parkinson's disease. Electroencephalography and clinical neurophysiology, 95(5), 309-317.
6.Al-Qazzaz, N. K., Ali, S. H. B. M., Ahmad, S. A., Chellappan, K., Islam, M. S., & Escudero, J. (2014). Role of EEG as biomarker in the early detection and classification of dementia. The Scientific World Journal, 2014(1), 906038.
Mehmood A, Yang S, Feng Z, Wang M, Ahmad AS, Khan R, et al. A transfer learning approach for early diagnosis of Alzheimer’s disease on MRI images. Neuroscience. 2021;460:43–2. https://doi.org/10.1016/j.neuroscience.2021.01.002.
7.https://www.springernature.com/gp/open-research/about/the-fundamentals-of-open-access-and-open-research
Murugan, S., Venkatesan, C., Sumithra, M. G., Gao, X. Z., Elakkiya, B., Akila, M., & Manoharan, S. (2021). DEMNET: A deep learning model for early diagnosis of Alzheimer diseases and dementia from MR images. Ieee Access, 9, 90319-90329.
8.Dauwels, J., Vialatte, F., & Cichocki, A. (2010). Diagnosis of Alzheimer's disease from EEG signals: where are we standing?. Current Alzheimer Research, 7(6), 487-505.
9.Babiloni, C., Del Percio, C., Lizio, R., Noce, G., Cordone, S., Lopez, S., ... & Bonanni, L. (2017). Abnormalities of cortical neural synchronization mechanisms in patients with dementia due to Alzheimer's and Lewy body diseases: an EEG study. Neurobiology of aging, 55, 143-158.
10.Henderson, G., Ifeachor, E., Hudson, N., Goh, C., Outram, N., Wimalaratna, S., ... & Vecchio, F. (2006). Development and assessment of methods for detecting dementia using the human electroencephalogram. IEEE Transactions on Biomedical Engineering, 53(8), 1557-1568.
Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
Indexed In
Similar Articles
Optimizing Traffic Sign Recognition Through Deep Learning Models
(2026)
DOI: 10.55041/ijsmt.v2i2.126
Integrating Digital Technologies to Preserve and Teach Indian Knowledge Systems
(2026)
DOI: 10.55041/ijsmt.v2i2.005
From Internal Mobility To Global Migration: The Kerala Experience
(2026)
DOI: 10.55041/ijsmt.v2i2.009
Previous Article
