An EEG-Guided Olfactory Interface: Prototype Design and Person-Specific Emotion-Decoding Validation.

Overview

1. Creative Computing Institute, University of the Arts London, London SE5 8UF, UK
2. Xiamen Academy of Arts and Design, Fuzhou University, Xiamen 361021, China

Abstract

Highlights: What are the main findings? In 39 adults, six induced emotional states were associated with small but FDR-corrected changes in frontal beta power, the beta/alpha ratio and global field power; device-derived on-line metrics showed larger effects. Six-class emotion decoding was above chance within participants (45.1%) and remained modest across participants after normalization (23.1%), indicating strong person specificity.

What are the implications of the main findings? The present study validates the EEG sensing and decoding path only; odor delivery, end-to-end feedback and regulatory efficacy were not experimentally tested. A deployable adaptive interface would require per-user calibration, confidence-aware decisions, multimodal robustness testing and quantitative actuator characterization.

Abstract: Just-in-time adaptive interventions require timely and low-burden state estimation, while olfaction offers a programmable output channel with limited attentional demand. We describe a prototype architecture that links electroencephalography (EEG)-based emotion estimation to a six-channel odorant device and evaluate only the EEG sensing and decoding module. Forty EEG sessions from 39 adults were recorded with a 14-channel Emotiv EPOC X headset (128 Hz) during six standardized emotion-induction conditions. No odor was administered. Band-power, frontal alpha asymmetry (FAA) and global field power (GFP) were analyzed with rank-based repeated-measures tests and explicit multiple-comparison correction. Emotion decoding used subject-aware cross-validation. Frontal beta power, the beta/alpha ratio and GFP differed across conditions after false-discovery-rate correction, although effect sizes were small (Kendall’s W = 0.089–0.155). On-line affective metrics showed larger effects (W = 0.130–0.365). Six-class accuracy was 45.1% ± 13.2% within participants (n = 29; chance 16.7%; p < 10−8) and 23.1% across participants after per-subject normalization (macro-F1 = 0.23; permutation p = 0.005). FAA did not differ. Consumer-headset EEG contained person-specific information about laboratory-induced states, but performance was not sufficient to establish a clinically usable regulator. The results validate neither a complete closed loop nor olfactory efficacy; end-to-end latency, artifact and temporal robustness, chemical characterization and controlled odor-regulation effects require prospective evaluation.

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

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Data Availability Statement

De-identified EEG features and analysis scripts are available from the corresponding author upon reasonable request.

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

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Version 2, 28 September 2026

• Funding: added Tsinghua University

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 8 keywords, 9 MeSH terms, 37 references.

Cite

This paper

Yang, J., & Lan, S. (2026). An EEG-Guided Olfactory Interface: Prototype Design and Person-Specific Emotion-Decoding Validation. Sensors (Basel, Switzerland), 26(16), 5237. https://doi.org/10.3390/s26165237

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