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Investigating Brain Lobe Biomarkers to Enhance Dementia Detection Using EEG Data
Siuly Siuly, Md. Nurul Ahad Tawhid, Yan Li, U. Rajendra Acharya, Muhammad Tariq Sadiq, Hua Wang. Cognitive Computation, 2025-04-01
Topic: Risk, prevention and care in population studies
Abstract Dementia is a growing global health concern that significantly impacts the quality of life for millions of individuals and imposes substantial burdens on healthcare systems. Early detection and accurate diagnosis are crucial for effective dementia management. Electroencephalography (EEG) has emerged as a non-invasive tool for identifying dementia-related abnormalities and assessing brain function. However, existing EEG-based methods often fail to pinpoint specific biomarkers, particularly brain lobe changes. Brain lobe analysis in EEG is essential for advancing dementia detection and improving diagnostic accuracy. This study aims to address this gap by exploring key brain lobes involved in dementia detection and classification, focusing on Alzheimer’s disease (AD) and Frontotemporal dementia (FTD). We introduce a Short-Time Fourier Transform to generate spectrogram images from EEG signals combined with Convolutional Neural Networks to identify the most critical brain lobes for enhanced dementia detection. We have applied Grad-CAM method to improve result interpretability and offer meaningful insights to the research community. Our experiments on OpenNeuro ds004504 EEG dataset for AD and FTD indicate that the parietal lobe exhibits the most significant changes in both conditions, achieving 95.72% accuracy for FTD and 92.25% for AD, followed by the temporal and frontal lobes. When applying the proposed framework to the entire brain region, we achieved 95.59% accuracy for AD and 93.14% for FTD. The findings from EEG-based brain lobe analysis aid experts in improving diagnostic and monitoring tools for neurodegenerative disorders, thereby advancing the understanding and clinical management of dementias like AD and FTD.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Siuly, S., Tawhid, M. N. A., Li, Y., Acharya, U. R., Sadiq, M. T., & Wang, H. (2025). Investigating Brain Lobe Biomarkers to Enhance Dementia Detection Using EEG Data. Cognitive Computation. https://doi.org/10.1007/s12559-025-10447-9
Vancouver
Siuly S, Tawhid MNA, Li Y, Acharya UR, Sadiq MT, Wang H. Investigating Brain Lobe Biomarkers to Enhance Dementia Detection Using EEG Data. Cognitive Computation. 2025. doi:10.1007/s12559-025-10447-9.
BibTeX
@article{siuly2025-d6660886,
author = {Siuly, Siuly and Tawhid, Md. Nurul Ahad and Li, Yan and Acharya, U. Rajendra and Sadiq, Muhammad Tariq and Wang, Hua},
title = {{Investigating Brain Lobe Biomarkers to Enhance Dementia Detection Using EEG Data}},
journal = {Cognitive Computation},
year = {2025},
doi = {10.1007/s12559-025-10447-9},
url = {https://doi.org/10.1007/s12559-025-10447-9}
}
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Key findings
- Abstract Dementia is a growing global health concern that significantly impacts the quality of life for millions of individuals and imposes substantial burdens on healthcare systems.
- We have applied Grad-CAM method to improve result interpretability and offer meaningful insights to the research community.
- Our experiments on OpenNeuro ds004504 EEG dataset for AD and FTD indicate that the parietal lobe exhibits the most significant changes in both conditions, achieving 95.72% accuracy for FTD and 92.25% for AD, followed by the temporal and frontal lobes.
Why it matters
This study used EEG data and convolutional neural networks to identify brain lobe biomarkers for dementia detection, finding that the parietal lobe exhibited the most significant changes in both Alzheimer's disease and frontotemporal dementia. The approach achieved high classification accuracy for both conditions.
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Study details
- Effect size95.72%; 92.25%; 95.59%
- Evidence strengthLow
How this study scored 42/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people and randomized / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Cognitive Computation) |
| Journal's citation record | 1 / 4 | Cognitive Computation: journal h-index 91 (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 | 4 / 5 | 2025 — within 3 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 | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 11 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 3.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 6.3× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 1 / 4 | Cited 11 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Hua Wang: h-index 80 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | University of Essex: institution h-index 464 (OpenAlex) |
| Multicenter collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 3 countries |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Total | 42 / 100 | — |
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