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by IQAC KIITKIIT Deemed to be University — Internal Quality Assurance Cell

Publication2026

Detection of Major Depressive Disorder from Resting State Single Channel EEG Data

The record

Type
Publication
Date
1 January 2026
Level
National
Journal
Procedia Computer Science
Volume
283
Pages
1347-1356
Published
1 January 2026
DOI
10.1016/j.procs.2026.06.212
Scopus ID
105046131943
ISSN
18770509
Authors
5
Open access
Yes
Citations
0
International co-authorship
No
Co-author countries
IN

Classification

Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.

UN Sustainable Development Goals

  1. 3.4 Reduce premature mortality from non-communicable diseases and promote mental health and well-being

    TargetPrimaryHigh confidenceJudged

    Single-channel EEG-based detection of major depressive disorder using machine learning achieves objective psychiatric diagnosis.

    High confidence
    The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
    Judged
    Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
    Produced by
    claude-haiku-4-5-20251001, under kortex-publication-v1

Viksit Bharat @2047

  1. vb-1.2 Health and well-being

    Sub-themePrimaryHigh confidenceJudged

    Mental health disorder detection technology supports mental well-being and psychiatric health outcomes.

    Produced by
    claude-haiku-4-5-20251001, under kortex-publication-v1

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KIIT Deemed to be University. “Detection of Major Depressive Disorder from Resting State Single Channel EEG Data”. Kortex.

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Detection of Major Depressive Disorder from Resting State Single Channel EEG Data · Kortex