Evidence map›Paper›PMID 36210993›Full record

ArticleComputational intelligence and neuroscience2022

A Novel Method Based on ERP and Brain Graph for the Simultaneous Assessment of Various Types of Attention.

Ali Esmaili Jami, Mohammad Ali Khalilzadeh, Majid Ghoshuni, Mohammad Mahdi Khalilzadeh

Open access · hybridAbstract read
In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 50% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
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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 1 institution in 1 country.

Ali Esmaili JamiDepartment of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.ORCID https://orcid.org/0000-0003-0115-3845
Mohammad Ali KhalilzadehDepartment of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.ORCID https://orcid.org/0000-0001-7718-6227
Majid GhoshuniDepartment of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.ORCID https://orcid.org/0000-0002-3780-6572
Mohammad Mahdi KhalilzadehDepartment of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.ORCID https://orcid.org/0000-0002-6615-2694
Islamic Azad University, Mashhad · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Assessment of attention is of great importance as one of human cognitive abilities. Although neuropsychological tests have been developed and used to evaluate the ability to pay attention, their validity and reliability have been reduced due to some limitations such as the presence of intervention factors, including human factors, limited range of languages, and cultural influences. Therefore, direct outputs of the brain system, represented by event-related potentials (ERPs), and the analysis of its function in cognitive activities have become very important as a complementary tool to assess various types of attention. This research tries to assess 4 types of attention including sustained, alternative, selective, and divided, using an integrated visual-auditory test and brain signals simultaneously. Thus, the electroencephalogram (EEG) data were recorded using 19 channels, and the integrated visual and auditory (IVA-AE) test was simultaneously performed on twenty-eight healthy volunteers including 22 male and 6 female subjects with the average age of 27 ± 5.3 years. Then ERPs related to auditory and visual stimuli with synchronous averaging technique were extracted. A topographic brain mapping (topo-map) was plotted for each frame of stimulation. Next, an optical flow method was implemented on different topo-maps to obtain motion vectors from one map to another. After obtaining the overall brain graph of an individual, some features were extracted and used as measures of local and global connectivity. The extracted features were consequently evaluated along with the parameters of the IVA test by support vector machine regression (SVM-R). The volume of attention was then quantified by combining the IVA parameters. Ultimately, estimation accuracy of each type of attention including focused attention (86.1%), sustained attention (83.4%), selective attention (80.9%), and divided attention (79.9%) was obtained. According to the present study, there is a significant relationship between response control and attention indicators of the IVA test as well as ERP brain signals.

Indexed as

ElectroencephalographyEvoked PotentialsAdultBrainBrain MappingFemaleHumansMaleReproducibility of ResultsYoung Adult

Identifiers

PMID36210993
PMCPMC9536935
OpenAlexW4297977792

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LicenceCC BY
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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.