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Effects of Distracting Task with Different Mental Workload on Steady-State Visual Evoked Potential Based Brain Computer Interfaces—an Offline Study

Overview of attention for article published in Frontiers in Neuroscience, February 2018
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Title
Effects of Distracting Task with Different Mental Workload on Steady-State Visual Evoked Potential Based Brain Computer Interfaces—an Offline Study
Published in
Frontiers in Neuroscience, February 2018
DOI 10.3389/fnins.2018.00079
Pubmed ID
Authors

Yawei Zhao, Jiabei Tang, Yong Cao, Xuejun Jiao, Minpeng Xu, Peng Zhou, Dong Ming, Hongzhi Qi

Abstract

Brain-computer interfaces (BCIs), independent of the brain's normal output pathways, are attracting an increasing amount of attention as devices that extract neural information. As a typical type of BCI system, the steady-state visual evoked potential (SSVEP)-based BCIs possess a high signal-to-noise ratio and information transfer rate. However, the current high speed SSVEP-BCIs were implemented with subjects concentrating on stimuli, and intentionally avoided additional tasks as distractors. This paper aimed to investigate how a distracting simultaneous task, a verbal n-back task with different mental workload, would affect the performance of SSVEP-BCI. The results from fifteen subjects revealed that the recognition accuracy of SSVEP-BCI was significantly impaired by the distracting task, especially under a high mental workload. The average classification accuracy across all subjects dropped by 8.67% at most from 1- to 4-back, and there was a significant negative correlation (maximumr= -0.48,p< 0.001) between accuracy and subjective mental workload evaluation of the distracting task. This study suggests a potential hindrance for the SSVEP-BCI daily use, and then improvements should be investigated in the future studies.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 18%
Student > Ph. D. Student 5 15%
Student > Bachelor 4 12%
Researcher 2 6%
Student > Doctoral Student 1 3%
Other 5 15%
Unknown 10 30%
Readers by discipline Count As %
Neuroscience 7 21%
Engineering 6 18%
Psychology 3 9%
Computer Science 2 6%
Medicine and Dentistry 1 3%
Other 2 6%
Unknown 12 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 February 2018.
All research outputs
#20,663,600
of 25,382,440 outputs
Outputs from Frontiers in Neuroscience
#9,459
of 11,542 outputs
Outputs of similar age
#360,233
of 470,360 outputs
Outputs of similar age from Frontiers in Neuroscience
#203
of 229 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,542 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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We're also able to compare this research output to 229 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.