Evidence map›Paper›PMID 29861712›Full record

ArticleComputational intelligence and neuroscience2018

Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network.

Jaehong Yoon, Jungnyun Lee, Mincheol Whang

Abstract read
In one paragraph

Article in Computational intelligence and neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Jaehong YoonDepartment of Biomedical Engineering, Duke University, Durham, NC 27708, USA.ORCID 0000-0003-2428-0724
Jungnyun LeeDepartment of Digital Media Engineering, Sangmyung University, Seoul 03016, Republic of Korea.ORCID 0000-0003-3236-4426
Mincheol WhangDepartment of Digital Media Engineering, Sangmyung University, Seoul 03016, Republic of Korea.ORCID 0000-0003-4301-9089

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Feature of event-related potential (ERP) has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain-computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects' ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.

Indexed as

Brain-Computer InterfacesEvoked PotentialsLiteracyNeural Networks, ComputerAdultBrainFemaleHumansLearning CurveMachine LearningMaleNeuropsychological TestsROC CurveSemanticsSignal Processing, Computer-AssistedTime Factors

Identifiers

PMID29861712
PMCPMC5976923

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.