Evidence map›Paper›PMID 42655545›Full record

ArticleSensors (Basel, Switzerland)2026

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

Jinge Yang, Suihong Lan

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jinge YangCreative Computing Institute, University of the Arts London, London SE5 8UF, UK.
Suihong LanXiamen Academy of Arts and Design, Fuzhou University, Xiamen 361021, China.ORCID 0009-0007-0722-8939

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Indexed as

ElectroencephalographyEmotionsSmellAdultFemaleHumansMaleOdorantsYoung Adultadaptive interfacesaffective computingdigital mental healthelectroencephalographyemotion recognitionhuman–computer interactionolfactory displayperson-specific calibration

Identifiers

PMID42655545
PMCPMC13517601

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.