MLSPred-bench: Transforming electroencephalography (EEG) datasets into machine learning-ready epileptic seizure prediction benchmarks.

Umair Mohammad, Fahad Saeed

Abstract read

In one paragraph

Article in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

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0citing papers in PubMed

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1 · What the graph read from it

What it found

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2 · The registry

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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

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5 · Who and what money

Authors and funding

2 authors.

Umair MohammadElectrical Computer and Biomedical Engineering Department, Union College, 807 Union St, Schenectady, NY 12308, US.

Fahad SaeedKnight Foundation School of Computing and Information Sciences, Florida International University, 11200 SW 8th St, Miami, FL 33199, USA.

Funding

Machine-Learning Models for big data omicsR35GM153434 · NIGMS · FLORIDA INTERNATIONAL UNIVERSITY · PI Fahad Saeed · 2024 to 2026

$1.1M

NIGMS NIH HHS R35 GM153434

6 · The paper itself

Abstract

Predicting epileptic seizures is a significantly more challenging task compared to seizure detection. However, most publicly available electroencephalography (EEG) datasets are geared towards detection because the ictal phase (main symptomatic period) is annotated. In contrast, prediction requires the availability of annotated preictal and interictal phases. To this end, we designed and developed a method called

Indexed as

Data WranglingDeep LearningElectroencephalographyEpilepsyMachine LearningSeizure Prediction

Identifiers

PMID40949826

DOI10.1016/j.mex.2025.103574

PMCPMC12423417

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LicenceCC BY-NC

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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 OpenQuestion graph.