Evidence map›Paper›PMID 28336927›Full record

ArticleScientific reports2017

Combination of Electroencephalography and Near-Infrared Spectroscopy in Evaluation of Mental Concentration during the Mental Focus Task for Wisconsin Card Sorting Test.

Che-Wei Chen, Chia-Wei Sun

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.4field-weighted citation impact, top 36% 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

5 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
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  3. 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

2 authors at 1 institution in 1 country.

Che-Wei ChenBiomedical Optical Imaging Lab, Department of Photonics, College of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan.
Chia-Wei SunBiomedical Optical Imaging Lab, Department of Photonics, College of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan. chiaweisun@nctu.edu.tw.ORCID 0000-0001-9769-8019
National Yang Ming Chiao Tung University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Near-infrared spectroscopy (NIRS) is a noninvasive neuroimaging tool for measuring evoked functional changes in brain oxygenation. Electroencephalography (EEG) can be used to evaluate the functionality of cortical connections and obtain information on regional cortical activity. Coregistration of EEG-NIRS is a recent technique that has been applied for measuring changes in electrical and hemodynamic activity in the human brain. EEG-NIRS coregistration facilitates the avoidance of misleading interpretations of NIRS, particularly in the diagnosis of neurological disorders. In this study, we investigated an approach for enhancing accuracy of NIRS by using EEG to monitor physiological activity during a mental focus task. Using the Wisconsin Card Sorting Test for the subjects mental focus task, we identified two trend types in the EEG and NIRS signals of normal subjects. These data can assist in understanding brain activation statuses and enable determining subjects' degree of mental concentration. If the data can be standardized for the diagnosis of neurological disorders, they can provide a new index to improve traditional methods (e.g., questionnaires) to assist clinical doctors in diagnosing cognitive disorders.

Indexed as

AttentionWisconsin Card Sorting TestCerebral CortexElectroencephalographyFemaleHumansMaleSpectroscopy, Near-InfraredYoung Adult

Identifiers

PMID28336927
PMCPMC5428406
OpenAlexW2596732220

What OpenQuestion holds

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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.