An EEG-Based Edge-AI Framework for Alzheimer's and Creutzfeldt-Jakob Disease Classification.
The 3 matches
- [1] § 2. Materials and Methods › 2.3. Experimental Setup ↔ EEG/sourcecodes/EEG_fair_nael.py, lines 16–67 · score 0.84 · d_model, encoder layers, CNN feature extractor, Transformer encoder, ELU, max
- [2] § 2. Materials and Methods › 2.2. Proposed Hybrid EEG Decoder System › 2.2.1. Custom 1D-CNN Feature Extractor ↔ EEG/sourcecodes/EEG_fair_nael.py, lines 16–67 · score 0.72 · CNN feature extractor, Transformer encoder, Linear, ELU, max, permuted
- [3] § 2. Materials and Methods › 2.1. EEG Signal Preprocessing and Dataset Construction ↔ EEG/sourcecodes/EEG_fair_nael.py, lines 472–511 · score 0.53 · random seed, CV, CNTRL, epoch, CJD, LOSO
Paper
Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC
The paper is loaded when this pane is shown.
The authors' code
Python · 512 lines · 17 KB · no license · 3 matches
EEG_fair_nael.py at commit 4d0647a, no license · at the source
Overview
- DICEAM, Mediterranea University of Reggio Calabria, 89125 Reggio Calabria, Italy; (N.M.); (F.C.M.)
- DICMaPI, University of Naples “Federico II”, 80125 Naples, Italy
- Department of Medical and Surgical Sciences, Magna Græcia University of Catanzaro, 88100 Catanazaro, Italy; (A.P.); (E.F.)
- Regional Epilepsy Centre, Great Metropolitan “Bianchi-Melacrino-Morelli” Hospital, 89124 Reggio Calabria, Italy
Abstract
Electroencephalography (EEG) has emerged as a promising non-invasive tool for the diagnosis of neurodegenerative disorders, and artificial intelligence (AI) has shown significant potential in this domain, as demonstrated by recent studies. However, strong inter-subject variability remains a major challenge, limiting the ability of AI-based models to learn disease-specific features that generalize across individuals, thereby hindering the development of clinically deployable subject-independent systems. In this work, we propose a cross-subject, AI-based EEG classification framework to distinguish between Alzheimer’s disease (AD), Creutzfeldt–Jakob disease (CJD), and healthy control subjects using clinical EEG data collected from a local hospital. A lightweight hybrid deep learning model is developed, combining a two-layer one-dimensional convolutional neural network with a two-layer Transformer encoder to capture both local temporal patterns and long-range dependencies in EEG signals. The proposed model achieves an average classification accuracy of 97%, representing a 3% improvement over a baseline model evaluated on a cohort of 36 subjects. To assess deployment feasibility in real-time clinical settings, the trained model is implemented and evaluated on an edge-AI platform (NVIDIA Jetson AGX Orin), demonstrating energy efficiency for the inference with a compact model footprint. These results indicate that the proposed approach provides an accurate, efficient, and practically deployable solution for subject-independent EEG-based classification of neurological disorders.
Reproduced under the paper's license (CC BY), from the paper cited above.
Repository
Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.
ai-lab-unirc/fair-nael-database
4d0647a7ab6317d58c318a932924bfdeeada9fec, 31 March 2026Availability: 1 check, the latest on 27 September 2026: the link answers
- 27 September 2026: the link answers
4 files, not copied: shown from their source
OSCR keeps no copy of these files: this repository has no license that allows it. The reader above shows each one from its source, fetched by your browser at commit 4d0647a, when its fingerprint is the one OSCR verified. How this works.
- EEG/
sourcecodes/ — Python, 512 lines, 3 matches, shown from its sourceEEG_fair_nael.py - TAC/
sourcecodes/ — Python, 124 lines, shown from its sourceraw_to_dicom.py - TAC/
sourcecodes/ — Python, 66 lines, shown from its sourceraw_to_nifti.py - README.md — Text, 88 lines, shown from its source
The paper's code and data availability statement is in the Data section.
Tracing map
Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.
What the map holds:
- 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
- 3 scripts, each with its path and the digest of its content;
- 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
- neither the text of the paper nor the code itself.
Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.
Data
No dataset and no data link were found in the paper.
Data Availability Statement
The analyzed dataset is available at the following link: https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.
Version 1, 28 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 8 keywords, 11 MeSH terms, 6 funders, 19 references.
Cite
This paper
Suffian, M., Ieracitano, C., Mammone, N., Pascarella, A., Ferlazzo, E., & Morabito, F. C. (2026). An EEG-Based Edge-AI Framework for Alzheimer's and Creutzfeldt-Jakob Disease Classification. Sensors (Basel, Switzerland), 26(10), 3274. https://
BibTeX
@article{suffian2026eeg,
author = {Suffian, Muhammad and Ieracitano, Cosimo and Mammone, Nadia and Pascarella, Angelo and Ferlazzo, Edoardo and Morabito, Francesco Carlo},
title = {{An EEG-Based Edge-AI Framework for Alzheimer's and Creutzfeldt-Jakob Disease Classification}},
journal = {Sensors (Basel, Switzerland)},
year = {2026},
month = may,
volume = {26},
number = {10},
pages = {3274},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {1424-8220},
doi = {10.3390/
url = {https://
pmid = {42198082},
pmcid = {PMC13211057}
}
RIS
TY - JOUR
AU - Suffian, Muhammad
AU - Ieracitano, Cosimo
AU - Mammone, Nadia
AU - Pascarella, Angelo
AU - Ferlazzo, Edoardo
AU - Morabito, Francesco Carlo
TI - An EEG-Based Edge-AI Framework for Alzheimer's and Creutzfeldt-Jakob Disease Classification
T2 - Sensors (Basel, Switzerland)
J2 - Sensors (Basel)
PY - 2026
DA - 2026/
VL - 26
IS - 10
SP - 3274
SN - 1424-8220
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/
UR - https://
LA - en
ER -
CSL-JSON
{
"id": "10.3390/
"type": "article-journal",
"title": "An EEG-Based Edge-AI Framework for Alzheimer's and Creutzfeldt-Jakob Disease Classification",
"container-title": "Sensors (Basel, Switzerland)",
"author": [
{
"family": "Suffian",
"given": "Muhammad"
},
{
"family": "Ieracitano",
"given": "Cosimo"
},
{
"family": "Mammone",
"given": "Nadia"
},
{
"family": "Pascarella",
"given": "Angelo"
},
{
"family": "Ferlazzo",
"given": "Edoardo"
},
{
"family": "Morabito",
"given": "Francesco Carlo"
}
],
"container-title-short":
"volume": "26",
"issue": "10",
"page": "3274",
"DOI": "10.3390/
"PMID": "42198082",
"PMCID": "PMC13211057",
"ISSN": "1424-8220",
"publisher": "Multidisciplinary Digital Publishing Institute (MDPI)",
"URL": "https://
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
21
]
]
}
}
The tracing map gets a citation of its own once an author has validated it and it has a DOI.
Similar papers
The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.
- [1] doi:10.1038/s41746-026-02778-0 [code]
- Trust-gated synthetic EEG augmentation reduces performance drops when generalizing to new patients.Journal: NPJ digital medicineIn common: PyTorch, scikit-learn, NumPy, EEG, 3 references
- [2] doi:10.3389/fnins.2026.1874302 [code]
- Architecture-data matching for EEG-EMG decoding: compact deep models match classical spectral decoders on the WAY-EEG-GAL grasp-and-lift dataset.Journal: Frontiers in neuroscienceIn common: PyTorch, scikit-learn, NumPy, EEG, 3 references
- [3] doi:10.1186/s13244-026-02296-3 [code]
- A pre-trained foundation model framework for multiplanar MRI classification of extramural vascular invasion and mesorectal fascia invasion in rectal cancer.Journal: Insights into imagingIn common: pydicom, NiBabel, PyTorch, 2 other tools, other condition
- [4] doi:10.1371/journal.pcbi.1014555 [code]
- Body surface potential driven personalisation of electrophysiological digital twins in hypertrophic cardiomyopathy.Journal: PLoS computational biologyIn common: pydicom, NiBabel, PyTorch, 2 other tools
- [5] doi:10.3389/frai.2026.1771088 [code]
- Few-shot deployment of pretrained MRI transformers in brain imaging tasks.Journal: Frontiers in artificial intelligenceIn common: pydicom, NiBabel, PyTorch, 2 other tools
- [6] doi:10.3390/biomimetics11060377
- PG-MCTFormer: A Prior-Guided Multi-Scale Convolutional Transformer for Interpretable Motor Imagery EEG Classification.Journal: Biomimetics (Basel, Switzerland)In common: EEG, 4 references
- [7] doi:10.1002/mp.70599 [code]
- Accurate estimation of intravoxel incoherent motion parameters based on implicit neural representation.Journal: Medical physicsIn common: pydicom, NiBabel, PyTorch, 1 other tool, other condition
- [8] doi:10.1080/07853890.2026.2685416 [code]
- Pulmonary and cerebral damage in COVID-19 survivors: is there any association?Journal: Annals of medicineIn common: pydicom, NiBabel, PyTorch, 1 other tool, other condition
- [9] doi:10.1038/s41586-026-10631-3 [code]
- A prognostic human brain network for diffuse midline glioma.Journal: NatureIn common: pydicom, NiBabel, scikit-learn, 1 other tool, other condition
- [10] doi:10.1002/hbm.70548 [code]
- Improved Multiscale Structural Mapping with Supervertex Vision Transformer for the Detection of Alzheimer's Disease Neurodegeneration.Journal: Human brain mappingIn common: NiBabel, PyTorch, scikit-learn, 1 other tool, Alzheimer's / dementia, 1 reference
Contribute
The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.
Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.
Claim this paper
Correct its record
Say what each link of this record is, remove the ones that are not the paper's, add the ones that are missing. The correction becomes a new version of the record, in its Versions section.
Validate its tracing map
You validate the map as this page shows it: 1 repository of the authors' code, each at its verified commit and with its license, 3 scripts, and 3 matches between paragraphs and code (see the Code and Map sections). It then receives a DOI on Zenodo, with you (your ORCID iD) and OSCR as its creators; the code itself is not deposited.
The map's fingerprint: sha256:77d812a4b60a5584…
Add the badge to its README
The badge links the code to this page. Copy one of these into the README of the paper's code: only you decide where it goes, and nothing is changed for you.
Markdown
[, paste the snippet at the top, then “Commit changes…” and, to review it first, “Create a new branch and start a pull request”. You open the pull request; OSCR asks for no permission.
Request its removal
To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).
Discussion, reproductions, activity
Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.
Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.
Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.