Evidence map›Paper›PMID 39555250›Full record

ArticleCognitive neurodynamics2024

Decoded EEG neurofeedback-guided cognitive reappraisal training for emotion regulation.

Linling Li, Xueying Gui, Gan Huang, Li Zhang, Feng Wan, Xue Han, Jianhong Wang, Dong Ni, Zhen Liang, Zhiguo Zhang

Abstract read
In one paragraph

Article in Cognitive neurodynamics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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

10 authors.

Linling Li *School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, 518060 China.ORCID 0000-0001-7767-7202
Xueying Gui *School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, 518060 China.
Gan HuangSchool of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, 518060 China.ORCID 0000-0001-9895-6163
Li ZhangSchool of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, 518060 China.ORCID 0000-0003-1641-7831
Feng WanDepartment of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China.ORCID 0000-0002-9359-0737
Xue HanDepartment of Mental Health, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, 518060 China.
Jianhong WangShenzhen Mental Health Center, Shenzhen Kangning Hospital, Shenzhen, 518060 China.
Dong NiSchool of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, 518060 China.ORCID 0000-0002-9146-6003
Zhen LiangSchool of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, 518060 China.ORCID 0000-0002-1749-2975
Zhiguo ZhangSchool of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518060 China.ORCID 0000-0001-7992-7965

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neurofeedback, when combined with cognitive reappraisal, offers promising potential for emotion regulation training. However, prior studies have predominantly relied on functional magnetic resonance imaging, which could impede its clinical feasibility. Furthermore, these studies have primarily focused on reducing negative emotions while overlooking the importance of enhancing positive emotions. In our current study, we developed a novel electroencephalogram (EEG) neurofeedback-guided cognitive reappraisal training protocol for emotion regulation. We recruited forty-two healthy subjects (20 females; 22.4 ± 2.2 years old) who were randomly assigned to either the neurofeedback group or the control group. We evaluated the efficacy of this newly proposed neurofeedback training approach in regulating emotions evoked by pictures with different valence levels (low positive and high negative). Initially, we trained an EEG-based emotion decoding model for each individual using offline data. During the training process, we calculated the subjects' real-time self-regulation performance based on the decoded emotional states and fed it back to the subjects as feedback signals. Our results indicate that the proposed decoded EEG neurofeedback-guided cognitive reappraisal training protocol significantly enhanced emotion regulation performance for stimuli with low positive valence. Additionally, wavelet energy and differential entropy features in the high-frequency band played a crucial role in emotion classification and were associated with neural plasticity changes induced by emotion regulation. These findings validate the beneficial effects of the proposed EEG neurofeedback protocol and offer insights into the neural mechanisms underlying its training effects. This novel decoded neurofeedback training protocol presents a promising cost-effective and non-invasive treatment technique for emotion-related mental disorders.

Indexed as

Cognitive reappraisalDecoded neurofeedbackElectroencephalogram (EEG)Emotion classificationEmotion regulation

Identifiers

PMID39555250
PMCPMC11564442

What OpenQuestion holds

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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.