Evidence map›Paper›PMID 33414701›Full record

ArticleFrontiers in neuroscience2020

Auditory Noise Leads to Increased Visual Brain-Computer Interface Performance: A Cross-Modal Study.

Jun Xie, Guozhi Cao, Guanghua Xu, Peng Fang, Guiling Cui, Yi Xiao, Guanglin Li, Min Li, Tao Xue, Yanjun Zhang and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neuroscience, 2020. 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

11 authors.

Jun XieSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.
Guozhi CaoSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.
Guanghua XuSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.
Peng FangCAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology & Shenzhen Engineering Laboratory of Neural Rehabilitation Technology, Shenzhen, China.
Guiling CuiNational Key Laboratory of Human Factors Engineering, China Astronauts Research and Training Center, Beijing, China.
Yi XiaoNational Key Laboratory of Human Factors Engineering, China Astronauts Research and Training Center, Beijing, China.
Guanglin LiCAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology & Shenzhen Engineering Laboratory of Neural Rehabilitation Technology, Shenzhen, China.
Min LiSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.
Tao XueSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.
Yanjun ZhangSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.
Xingliang HanSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Noise has been proven to have a beneficial role in non-linear systems, including the human brain, based on the stochastic resonance (SR) theory. Several studies have been implemented on single-modal SR. Cross-modal SR phenomenon has been confirmed in different human sensory systems. In our study, a cross-modal SR enhanced brain-computer interface (BCI) was proposed by applying auditory noise to visual stimuli. Fast Fourier transform and canonical correlation analysis methods were used to evaluate the influence of noise, results of which indicated that a moderate amount of auditory noise could enhance periodic components in visual responses. Directed transfer function was applied to investigate the functional connectivity patterns, and the flow gain value was used to measure the degree of activation of specific brain regions in the information transmission process. The results of flow gain maps showed that moderate intensity of auditory noise activated the brain area to a greater extent. Further analysis by weighted phase-lag index (wPLI) revealed that the phase synchronization between visual and auditory regions under auditory noise was significantly enhanced. Our study confirms the existence of cross-modal SR between visual and auditory regions and achieves a higher accuracy for recognition, along with shorter time window length. Such findings can be used to improve the performance of visual BCIs to a certain extent.

Indexed as

auditory noisebrain–computer interface (BCI)cross-modal stochastic resonancefunctional connectivityphase synchronizationsteady-state motion visual evoked potential (SSMVEP)

Identifiers

PMID33414701
PMCPMC7783197

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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.