Evidence map›Paper›PMID 37900730›Full record

ArticleFrontiers in human neuroscience2023

The increasing instance of negative emotion reduce the performance of emotion recognition.

Xiaomin Wang, Shaokai Zhao, Yu Pei, Zhiguo Luo, Liang Xie, Ye Yan, Erwei Yin

Abstract read
In one paragraph

Article in Frontiers in human neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Xiaomin WangAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
Shaokai ZhaoDefense Innovation Institute, Academy of Military Sciences (AMS), Beijing, China.
Yu PeiDefense Innovation Institute, Academy of Military Sciences (AMS), Beijing, China.
Zhiguo LuoDefense Innovation Institute, Academy of Military Sciences (AMS), Beijing, China.
Liang XieDefense Innovation Institute, Academy of Military Sciences (AMS), Beijing, China.
Ye YanDefense Innovation Institute, Academy of Military Sciences (AMS), Beijing, China.
Erwei YinDefense Innovation Institute, Academy of Military Sciences (AMS), Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Emotion recognition plays a crucial role in affective computing. Recent studies have demonstrated that the fuzzy boundaries among negative emotions make recognition difficult. However, to the best of our knowledge, no formal study has been conducted thus far to explore the effects of increased negative emotion categories on emotion recognition. Methods: A dataset of three sessions containing consistent non-negative emotions and increased types of negative emotions was designed and built which consisted the electroencephalogram (EEG) and the electrocardiogram (ECG) recording of 45 participants. Results: The results revealed that as negative emotion categories increased, the recognition rates decreased by more than 9%. Further analysis depicted that the discriminative features gradually reduced with an increase in the negative emotion types, particularly in the θ, α, and β frequency bands. Discussion: This study provided new insight into the balance of emotion-inducing stimuli materials.

Indexed as

affective computingECGEEGemotion recognitionexperimental protocol designingnegative emotion

Identifiers

PMID37900730
PMCPMC10611512

What OpenQuestion holds

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Registered trials

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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.