Spectral Entropy Can Predict Changes of Working Memory Performance Reduced by Short-Time Training in the Delayed-Match-to-Sample Task

Spectral Entropy Can Predict Changes of Working Memory Performance Reduced by Short-Time Training in the Delayed-Match-to-Sample Task

Spectral entropy, which was generated by applying the Shannon entropy concept to the power distribution of the Fourier-transformed electroencephalograph (EEG), was utilized to measure the uniformity of power spectral density underlying EEG when subjects performed the working memory tasks twice, i.e....

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Bibliographic Details
Main Authors: Yin Tian, Huiling Zhang, Wei Xu, Haiyong Zhang, Li Yang, Shuxing Zheng, Yupan Shi
Format: Article
Language:English
Published: Frontiers Media S.A. 2017-08-01
Series:Frontiers in Human Neuroscience
Subjects:
spectral entropy
WM performance
SVR
prediction
classification
BCIs
Online Access:http://journal.frontiersin.org/article/10.3389/fnhum.2017.00437/full

Internet

http://journal.frontiersin.org/article/10.3389/fnhum.2017.00437/full

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