Shuang Liu [5]Shuangyin Liu [1]
  1.  9
    The Effects of Transcranial Direct Current Stimulation (tDCS) on Working Memory Training in Healthy Young Adults.Yufeng Ke, Ningci Wang, Jiale Du, Linghan Kong, Shuang Liu, Minpeng Xu, Xingwei An & Dong Ming - 2019 - Frontiers in Human Neuroscience 13:421402.
    Working memory (WM) is a fundamental cognitive ability to support complex thought, but it is limited in capacity. WM training has shown the potential to make benefit for those in need. Many studies have shown the potential of transcranial direct current stimulation (tDCS) to transiently enhance WM performance by delivering low current to the brain cortex of interest via electrodes on the scalp. TDCS has also been revealed as a promising intervention to augment WM training in a few studies. However, (...)
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  2.  21
    Cross-Dataset Variability Problem in EEG Decoding With Deep Learning.Lichao Xu, Minpeng Xu, Yufeng Ke, Xingwei An, Shuang Liu & Dong Ming - 2020 - Frontiers in Human Neuroscience 14.
  3.  41
    Incorporation of Multiple-Days Information to Improve the Generalization of EEG-Based Emotion Recognition Over Time.Shuang Liu, Long Chen, Dongyue Guo, Xiaoya Liu, Yue Sheng, Yufeng Ke, Minpeng Xu, Xingwei An, Jiajia Yang & Dong Ming - 2018 - Frontiers in Human Neuroscience 12.
  4.  51
    An EEG-based mental workload estimator trained on working memory task can work well under simulated multi-attribute task.Yufeng Ke, Hongzhi Qi, Feng He, Shuang Liu, Xin Zhao, Peng Zhou, Lixin Zhang & Dong Ming - 2014 - Frontiers in Human Neuroscience 8.
  5.  12
    Improving the Cross-Subject Performance of the ERP-Based Brain–Computer Interface Using Rapid Serial Visual Presentation and Correlation Analysis Rank.Shuang Liu, Wei Wang, Yue Sheng, Ludan Zhang, Minpeng Xu & Dong Ming - 2020 - Frontiers in Human Neuroscience 14.
  6.  10
    Deep Convolutional Generative Adversarial Network and Convolutional Neural Network for Smoke Detection.Hang Yin, Yurong Wei, Hedan Liu, Shuangyin Liu, Chuanyun Liu & Yacui Gao - 2020 - Complexity 2020:1-12.
    Real-time smoke detection is of great significance for early warning of fire, which can avoid the serious loss caused by fire. Detecting smoke in actual scenes is still a challenging task due to large variance of smoke color, texture, and shapes. Moreover, the smoke detection in the actual scene is faced with the difficulties in data collection and insufficient smoke datasets, and the smoke morphology is susceptible to environmental influences. To improve the performance of smoke detection and solve the problem (...)
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