Evidence map›Paper›PMID 34295217›Full record

ArticleFrontiers in neuroscience2021

A Novel Cognition-Guided Neurofeedback BCI Dataset on Nicotine Addiction.

Junjie Bu, Chang Liu, Huixing Gou, Hefan Gan, Yan Cheng, Mengyuan Liu, Rui Ni, Zhen Liang, Guanbao Cui, Ginger Qinghong Zeng and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.5field-weighted citation impact, top 41% of its field
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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. 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

11 authors at 4 institutions in 2 countries.

Junjie BuDepartment of Radiology, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China.
Chang LiuDepartment of Radiology, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China.
Huixing GouDepartment of Radiology, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China.
Hefan GanDepartment of Radiology, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China.
Yan ChengDepartment of Psychology, School of Humanities and Social Science, University of Science and Technology of China, Hefei, China.
Mengyuan LiuDepartment of Psychology, School of Humanities and Social Science, University of Science and Technology of China, Hefei, China.
Rui NiDepartment of Life Sciences, Imperial College London, London, United Kingdom.
Zhen LiangDepartment of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China.
Guanbao CuiInstitute of Advanced Technology, University of Science and Technology of China, Hefei, China.
Ginger Qinghong ZengDepartment of Radiology, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China.
Xiaochu ZhangDepartment of Radiology, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China.
University of Science and Technology of China · CNAnhui Medical University · CNAnhui Mental Health Center · CNImperial College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Compared with the traditional neurofeedback paradigm, the cognition-guided neurofeedback brain-computer interface (BCI) is a novel paradigm with significant effect on nicotine addiction. However, the cognition-guided neurofeedback BCI dataset is extremely lacking at present. This paper provides a BCI dataset based on a novel cognition-guided neurofeedback on nicotine addiction. Twenty-eight participants are recruited and involved in two visits of neurofeedback training. This cognition-guided neurofeedback includes two phases: an offline classifier construction and a real-time neurofeedback training. The original electroencephalogram (EEG) raw data of two phases are provided and evaluated in this paper. The event-related potential (ERP) amplitude and channel waveform suggest that our BCI dataset is of good quality and consistency. During neurofeedback training, the participants' smoking cue reactivity patterns have a significant reduction. The mean accuracy of the multivariate pattern analysis (MVPA) classifier can reach approximately 70%. This novel cognition-guided neurofeedback BCI dataset can be used to develop comparisons with other neurofeedback systems and provide a reference for the development of other BCI algorithms and neurofeedback paradigms on addiction.

Indexed as

brain-computer interfacecognition-guided neurofeedbackelectro encephalogramnicotine addictionpublic dataset

Identifiers

PMID34295217
PMCPMC8290081
OpenAlexW3182932823

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.