Evidence map›Paper›PMID 35874640›Full record

ArticleBehavioural neurology2022

Behavioral Analysis of EEG Signals in Loss-Gain Decision-Making Experiments.

Jiaquan Shen, Ningzhong Liu, Deguang Li, Binbin Zhang

Open access · goldAbstract read
In one paragraph

Article in Behavioural neurology, 2022. 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
0.2field-weighted citation impact, top 51% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

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

4 authors at 2 institutions in 1 country.

Jiaquan ShenSchool of Information Science, Luoyang Normal University, Luoyang 471022, China.ORCID https://orcid.org/0000-0001-5336-7111
Ningzhong LiuCollege of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Jiangsu Nanjing 211106, China.ORCID https://orcid.org/0000-0002-6735-7130
Deguang LiSchool of Information Science, Luoyang Normal University, Luoyang 471022, China.ORCID https://orcid.org/0000-0002-8653-813X
Binbin ZhangSchool of Information Science, Luoyang Normal University, Luoyang 471022, China.ORCID https://orcid.org/0000-0002-4773-1455
Luoyang Normal University · CNNanjing University of Aeronautics and Astronautics · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Extraction and analysis of the EEG (electroencephalograph) information features generated during behavioral decision-making can provide a better understanding of the state of mind. Previous studies have focused more on the brainwave features after behavioral decision-making. In fact, the EEG before decision-making is more worthy of our attention. In this study, we introduce a new index based on the reaction time of subjects before decision-making, called the Prestimulus Time (PT), which have important reference value for the study of cognitive function, neurological diseases, and other fields. In our experiments, we use a wearable EEG feature signal acquisition device and a systematic reward and punishment experiment to obtain the EEG features before and after behavioral decision-making. The experimental results show that the EEG generated after behavioral decision due to loss is more intense than that generated by gain in the medial frontal cortex (MFC). In addition, different characteristics of EEG signals are generated prior to behavioral decisions because people have different expectations of the outcome. It will produce more significant negative-polarity event-related potential (ERP) in the forebrain area when the humans are optimistic about the outcomes.

Indexed as

Decision MakingElectroencephalographyEvoked PotentialsFrontal LobeHumansReward

Identifiers

PMID35874640
PMCPMC9307401
OpenAlexW4285590947

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.