EEG emotion-recognition datasets for neuroarchitecture: an appraisal of openness, AI-readiness and ontology maturity

Overview

1. LINK4EEG Lab, School of Architecture, Hanyang University, Seoul, Republic of Korea
2. School of Built Environment, Faculty of Arts, Design & Architecture, The University of New South Wales, Sydney, NSW, Australia
3. ADCC, School of Architecture, Hanyang University, Seoul, Republic of Korea

Abstract

Introduction: Neuroarchitecture studies how designed space shapes emotion, often with electroencephalography (EEG), and computational models that decode emotion from EEG must be trained on labelled recordings. Yet none of the widely shared EEG emotion datasets was recorded while people moved through designed spaces, and the few spatial recordings we found, all released since 2023, are single studies, each cited at most once. Researchers therefore borrow general emotion datasets, recorded from seated participants watching films, listening to music, playing games or conversing, and must decide whether each can be found, obtained and reused for transfer learning. This review contributes a reusable method for that decision and applies it.

Methods: Following PRISMA 2020 with the dataset as the unit of analysis, two search passes identified 27 publicly obtainable datasets (22 in the first, 5 in the second), none designed for spatial research. A published codebook of 52 criteria defines three instruments: openness, judged against the FAIR principles with access class recorded separately; AI-readiness, the equally weighted mean of three sub-scores (label information, fit to standard tasks and alignment with other data), which describes usefulness as a transfer source; and ontology maturity, on five levels, which describes how far emotion labels can be mapped onto other vocabularies. Every identifier and access route was checked.

Results: Openness was weakest in Interoperability (2.044 against a FAIR composite of 2.857), and only 8 datasets were fully open. A dataset-level identifier was missing for 13 of 27 datasets (48.1%), and three widely used datasets could not be reached through their published routes at a re-check on 11 September 2026. AI-readiness averaged 2.684 on a 1.0–4.0 scale (median 2.60, range 2.10–3.33), and ontology levels concentrated at Level 3 (16 of 27), with none at Level 4 or 5. Openness and readiness were nearly unrelated (ρ = 0.08), whereas ontology level tracked readiness (ρ = 0.68), partly because both read the label structure.

Discussion: Open access alone is therefore a poor guide to reuse. The method, codebook and scripts are published so that researchers in fields without dedicated data can test the datasets they plan to reuse.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• doi:10.18112/openneuro.ds003004.v1.1.1, at OpenNeuro; found in the references

Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author/s.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 4 November 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 3 authors, 8 keywords, 50 references.

Cite

This paper

Ji, S. Y., Kim, M. K., & Jun, H. J. (2026). EEG emotion-recognition datasets for neuroarchitecture: an appraisal of openness, AI-readiness and ontology maturity. Frontiers in neuroinformatics, 20, 1971441.

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