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The Scalp Time-Varying Networks of N170: Reference, Latency, and Information Flow

Overview of attention for article published in Frontiers in Neuroscience, April 2018
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Title
The Scalp Time-Varying Networks of N170: Reference, Latency, and Information Flow
Published in
Frontiers in Neuroscience, April 2018
DOI 10.3389/fnins.2018.00250
Pubmed ID
Authors

Yin Tian, Wei Xu, Huiling Zhang, Kin Y. Tam, Haiyong Zhang, Li Yang, Zhangyong Li, Yu Pang

Abstract

Using the scalp time-varying network method, the present study is the first to investigate the temporal influence of the reference on N170, a negative event-related potential component (ERP) appeared about 170 ms that is elicited by facial recognition, in the network levels. Two kinds of scalp electroencephalogram (EEG) references, namely, AR (average of all recording channels) and reference electrode standardization technique (REST), were comparatively investigated via the time-varying processing of N170. Results showed that the latency and amplitude of N170 were significantly different between REST and AR, with the former being earlier and smaller. In particular, the information flow from right temporal-parietal P8 to left P7 in the time-varying network was earlier in REST than that in AR, and this phenomenon was reproduced by simulation, in which the performance of REST was closer to the true case at source level. These findings indicate that reference plays a crucial role in ERP data interpretation, and importantly, the newly developed approximate zero-reference REST would be a superior choice for precise evaluation of the scalp spatio-temporal changes relating to various cognitive events.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 18%
Professor 3 18%
Student > Bachelor 2 12%
Student > Master 2 12%
Lecturer > Senior Lecturer 1 6%
Other 3 18%
Unknown 3 18%
Readers by discipline Count As %
Neuroscience 4 24%
Psychology 3 18%
Engineering 2 12%
Sports and Recreations 1 6%
Agricultural and Biological Sciences 1 6%
Other 2 12%
Unknown 4 24%
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 23 April 2018.
All research outputs
#19,951,180
of 25,382,440 outputs
Outputs from Frontiers in Neuroscience
#8,672
of 11,542 outputs
Outputs of similar age
#250,555
of 340,784 outputs
Outputs of similar age from Frontiers in Neuroscience
#208
of 248 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
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