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Standardizing Number of EEG Sensors for AI-Driven Dementia Detection
Quoc-Toan Nguyen. IEEE Sensors Letters, 2025-04-14
Topic: Risk, prevention and care in population studies
Alzheimer's Disease (AD), the most prevalent dementia subtype, early detection is paramount for slowing disease progression because cures are not available. Artificial Intelligence (AI) has gained significant attention for AD detection, particularly through Electroencephalography (EEG)—a cost-effective, accessible technique that records brain activity from sensors placed on the scalp. However, the variability in EEG sensor configurations across different settings poses a significant challenge, as typical AI models require different models for specific setups. To address this limitation, this study explores using EEG microstates to standardise sensor configurations and propose a new AI model, E-FastKAN, to validate sensor generalisation, ensuring effectiveness regardless of the original number of sensors. Using data from 115 participants with 575 samples from high-density and low-density sensor setups, this study aims to open a new approach to reduce costs, increase accessibility, and broaden the usability of AI-driven dementia detection.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Nguyen, Q.-T. (2025). Standardizing Number of EEG Sensors for AI-Driven Dementia Detection. IEEE Sensors Letters. https://doi.org/10.1109/lsens.2025.3560259
Vancouver
Nguyen Q.-T. Standardizing Number of EEG Sensors for AI-Driven Dementia Detection. IEEE Sensors Letters. 2025. doi:10.1109/lsens.2025.3560259.
BibTeX
@article{nguyen2025-2348e94a,
author = {Nguyen, Quoc-Toan},
title = {{Standardizing Number of EEG Sensors for AI-Driven Dementia Detection}},
journal = {IEEE Sensors Letters},
year = {2025},
doi = {10.1109/lsens.2025.3560259},
url = {https://doi.org/10.1109/lsens.2025.3560259}
}
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.
Key findings
- Artificial Intelligence (AI) has gained significant attention for AD detection, particularly through Electroencephalography (EEG)—a cost-effective, accessible technique that records brain activity from sensors placed on the scalp.
- However, the variability in EEG sensor configurations across different settings poses a significant challenge, as typical AI models require different models for specific setups.
Why it matters
This study explores using EEG microstates to standardize sensor configurations and proposes a new AI model to make dementia detection work regardless of the number of sensors. It aims to reduce costs and increase accessibility of AI-driven detection, not to treat, cure or prevent Alzheimer's disease.
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Study details
- Participants115
- Evidence strengthLow
How this study scored 38/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported 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 36 of the 96 points we could judge it on, which is 38 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (IEEE Sensors Letters) |
| Journal's citation record | 0 / 4 | IEEE Sensors Letters: journal h-index 51 (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 | 2 / 4 | 115 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 7 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 2.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 4.0× 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 7 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | 0 / 3 | FPT University: institution h-index 70 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 36 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 36 of the 96 points we could judge it on is 38 out of 100. |
| Total | 38 / 100 | — |
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