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Integrating convolutional variational autoencoders and the Gaussian mixture model for efficient manifold learning and clustering of spatially preserved EEG topographic maps

Crossref DOI link: https://doi.org/10.1186/s40708-026-00327-9

Published Online: 2026-08-24

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Faremi, Saheed

Longo, Luca
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Text and Data Mining valid from 2026-08-24

Accepted Manuscript valid from 2026-08-24
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Article History

Received: 13 December 2025

Accepted: 3 August 2026

First Online: 24 August 2026

Declarations

Ethics approval and consent to participate: This study is a secondary analysis of the publicly available and anonymised LEMON (Leipzig Mind-Brain-Body) dataset distributed by the Max Planck Institute for Human Cognitive and Brain Sciences. The original data collection was conducted with appropriate ethical oversight, and all participants provided informed consent.

Consent for publication: Not applicable, as this study utilised a pre-existing, anonymised public dataset where individual-level data is not identifiable.

Competing interest: The author declares that they have no competing interest.

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