Evidence map›Paper›PMID 42548576›Full record

ArticleFrontiers in neurology

EEGDecoder-x: an explainable deep learning framework for cross-subject EEG-based detection of Alzheimer's and Creutzfeldt-Jakob disease.

Muhammad Suffian, Nadia Mammone, Cosimo Ieracitano, Giovanbattista Gaspare Tripodi, Angelo Pascarella, Edoardo Ferlazzo, Francesco Carlo Morabito

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Article in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Muhammad SuffianDICEAM, Mediterranea University of Reggio Calabria, Reggio Calabria, Italy.
Nadia MammoneDICEAM, Mediterranea University of Reggio Calabria, Reggio Calabria, Italy.
Cosimo IeracitanoDICMaPI, University of Naples "Federico II", Naples, Italy.
Giovanbattista Gaspare TripodiNeurology Unit, Great Metropolitan "Bianchi-Melacrino-Morelli" Hospital, Reggio Calabria, Italy.
Angelo PascarellaDepartment of Medical and Surgical Sciences, Magna Græcia University of Catanzaro, Catanazaro, Italy.
Edoardo FerlazzoDepartment of Medical and Surgical Sciences, Magna Græcia University of Catanzaro, Catanazaro, Italy.
Francesco Carlo MorabitoDICEAM, Mediterranea University of Reggio Calabria, Reggio Calabria, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early detection of neurodegenerative diseases is critical. Distinguishing early-stage Creutzfeldt-Jakob disease (CJD) from "mimics" like Alzheimer's disease (AD) remains a major challenge; while EEG is valuable in advanced CJD, early-stage abnormalities are often non-specific and overlap with other rapidly progressive dementias. Deep learning offers promising EEG-based diagnostic solutions, but clinical adoption requires transparent decision-making, the interpretability of the features learned by deep learning models is equally important. In this context, careful model design and explainability are essential. In this paper, we propose a novel interpretable framework,

Indexed as

Alzheimer's diseasebrain-computer interfaceCreutzfeldt-Jakob diseasecross-subject decodingelectroencephalographyexplainable AI

Identifiers

PMID42548576
PMCPMC13429416

What Socratic holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.