OSCR

Adaptive drift-aware multi-stage deep learning framework for EEG-based schizophrenia diagnosis.

Overview

Authors: Gran Badshah1, Anurag Sinha2, Himanshu Bansal3, Yash Varshney4, Omar Alqahtani1, Ayushman Srivastava5
ORCID iDs: Yash Varshney
  1. Department of Computer Science, King Khalid University, Abha, 61413 Saudi Arabia
  2. School of Computing and Information Science, Indira Gandhi National Open University (IGNOU), New Delhi, India
  3. Department of Computer Science and Engineering, KIET Group of Institutions, Ghaziabad, Delhi-NCR India
  4. Lodha Genius Programme, Ashoka University, Sonepat, Haryana 131029 India
  5. Nile Dews Consultant, RMSI Pvt. Ltd, Entebbe, Uganda
Journal: BioData mining, volume 19, issue 1, article 38
Dates: received 30 December 2025; accepted 24 April 2026; published online 11 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s13040-026-00560-2 · PMID 42116189 · PMCID PMC13170158 · OpenAlex W7160852578
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: EEG (modality), schizophrenia / psychosis (population), clinical / translational (subfield)
Methods: Preprocessing, Smoothing, state filtering, decompositions, Machine learning, Spectral & time-frequency, Evoked potentials, Connectivity, Statistics
Keywords: Electroencephalography, Schizophrenia, Signal processing, Long short-term memory (LSTM), Attention networks
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 63 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

The paper links to its data, not to its authors' code: see the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it says that the data are available on request

Read it in the paper: doi.org/10.1186/s13040-026-00560-2.

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 2, 28 September 2026

  • Funding: added King Khalid University

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 54 references.

Cite

This paper

Badshah, G., Sinha, A., Bansal, H., Varshney, Y., Alqahtani, O., & Srivastava, A. (2026). Adaptive drift-aware multi-stage deep learning framework for EEG-based schizophrenia diagnosis. BioData mining, 19(1), 38. https://doi.org/10.1186/s13040-026-00560-2

BibTeX

@article{badshah2026adaptive,
author = {Badshah, Gran and Sinha, Anurag and Bansal, Himanshu and Varshney, Yash and Alqahtani, Omar and Srivastava, Ayushman},
title = {{Adaptive drift-aware multi-stage deep learning framework for EEG-based schizophrenia diagnosis}},
journal = {BioData mining},
year = {2026},
month = may,
volume = {19},
number = {1},
pages = {38},
publisher = {BMC},
issn = {1756-0381},
doi = {10.1186/s13040-026-00560-2},
url = {https://doi.org/10.1186/s13040-026-00560-2},
pmid = {42116189},
pmcid = {PMC13170158}
}

RIS

TY - JOUR
AU - Badshah, Gran
AU - Sinha, Anurag
AU - Bansal, Himanshu
AU - Varshney, Yash
AU - Alqahtani, Omar
AU - Srivastava, Ayushman
TI - Adaptive drift-aware multi-stage deep learning framework for EEG-based schizophrenia diagnosis
T2 - BioData mining
J2 - BioData Min
PY - 2026
DA - 2026/05/11
VL - 19
IS - 1
SP - 38
SN - 1756-0381
PB - BMC
DO - 10.1186/s13040-026-00560-2
UR - https://doi.org/10.1186/s13040-026-00560-2
LA - en
ER -

CSL-JSON

{
"id": "10.1186/s13040-026-00560-2",
"type": "article-journal",
"title": "Adaptive drift-aware multi-stage deep learning framework for EEG-based schizophrenia diagnosis",
"container-title": "BioData mining",
"author": [
{
"family": "Badshah",
"given": "Gran"
},
{
"family": "Sinha",
"given": "Anurag"
},
{
"family": "Bansal",
"given": "Himanshu"
},
{
"family": "Varshney",
"given": "Yash"
},
{
"family": "Alqahtani",
"given": "Omar"
},
{
"family": "Srivastava",
"given": "Ayushman"
}
],
"container-title-short": "BioData Min",
"volume": "19",
"issue": "1",
"page": "38",
"DOI": "10.1186/s13040-026-00560-2",
"PMID": "42116189",
"PMCID": "PMC13170158",
"ISSN": "1756-0381",
"publisher": "BMC",
"URL": "https://doi.org/10.1186/s13040-026-00560-2",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
11
]
]
}
}

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:10.3390/bioengineering13040430 [code]
Configurable Modular EEG Classification Framework with Multiscale Features and Ensemble Learning: A Reproducible Evaluation for Schizophrenia Detection.
Journal: Bioengineering (Basel, Switzerland)
In common: schizophrenia / psychosis, EEG, 8 references
[2] doi:
Effect of Emotional States on EEG-Based Biometric Identification: A Comparative Study of Classifiers
Journal: Bioengineering (Basel, Switzerland)
In common: EEG, 5 references
[3] doi:10.3390/s26134004 [code]
Automated Anxiety Detection System Integrating a Brain-Computer Interface for Neurofeedback Applications.
Journal: Sensors (Basel, Switzerland)
In common: EEG, 4 references
[4] doi:10.1007/s10916-026-02374-5 [code]
Attention-Enhanced U-Net for Sensor-Efficient High-Density EEG Reconstruction in Wearable Brain Monitoring Systems.
Journal: Journal of medical systems
In common: EEG, 2 references
[5] doi:10.1371/journal.pone.0348608
A novel approach for the EEG-driven assessment of divided attention through mutual information theory: A case study at the wheel.
Journal: PloS one
In common: EEG, 2 references
[6] doi:10.3390/s26092777 [code]
Comparison of Point-and-Click Performance Between the Brainfingers BCI and the Mouse.
Journal: Sensors (Basel, Switzerland)
In common: EEG, 2 references
[7] doi:10.1038/s41398-026-04030-5
Loss of regional theta differentiation in TMS-EEG response marks network dysfunction in psychosis risk.
Journal: Translational psychiatry
In common: schizophrenia / psychosis, EEG, clinical / translational, 1 reference
[8] doi:10.3390/bioengineering13050586 [code]
Robust Multi-Site ADHD Classification via GraphSAGE-Based Functional Connectivity Modeling from rs-fMRI.
Journal: Bioengineering (Basel, Switzerland)
In common: 2 references
[9] doi:10.3390/e28060644 [code]
Riemannian Geometry for Noise-Robust Covariance Network Analysis of Schizophrenia EEG: Geometric-Entropic Signatures of Dysconnectivity.
Journal: Entropy (Basel, Switzerland)
In common: schizophrenia / psychosis, EEG, 1 reference
[10] doi:10.1371/journal.pcbi.1014304 [code]
Linking reduced prefrontal microcircuit inhibition in schizophrenia to EEG biomarkers in silico.
Journal: PLoS computational biology
In common: schizophrenia / psychosis, EEG, 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.

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.