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Multivariate empirical mode decomposition and multiscale entropy analysis of EEG signals from SSVEP-based BCI system

机译：基于SSVEP的BCI系统的EEG信号多变量经验模式分解和多尺度熵分析

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摘要

The steady state visual evoked potential (SSVEP)-based Brain-Computer Interface (BCI) has been employed in the brain-controlled wheelchair system for patients with severe dyskinesia disease. However, a long-time operation brings about users fatigue, leading to a decrease of performance of the BCI system in practical applications. The characterization of the fatigued mechanism and the improvement of the SSVEP classification accuracy remains a challenging problem of significant importance. In this letter, we first conduct SSVEP experiments to acquire the EEG signals during both normal and fatigued states. Then we develop a novel framework, which integrates the advantages of multivariate empirical mode decomposition (MEMD) and Support Vector Machine (SVM), for improving the SSVEP classification accuracy, especially during the fatigued state. The results suggest that the novel framework enables us to obtain a higher SSVEP classification accuracy compared with the method without. MEMD. Furthermore, in order to reveal the fatigued behavior, we use the multivariate multiscale sample entropy (MMSE) to analyze the multi-channel EEG signals corresponding to normal and fatigued states. We interestingly find that the MMSE values in the fatigued state are lower than that in the normal state, which reflects the increase of regularity in SSVEP signals during the fatigued state. That is, a greater synchronization of neural assemblies is required to realize cognitive impairment when fatigue happens. The knowledge for the understanding of the brain fatigued behavior underlying SSVEP-based BCI experiments is gained by our analysis. Copyright (C) EPLA, 2018
机译：稳态视觉诱发电位（SSVEP）基础脑电接口（BCI）已在脑控制的轮椅系统中用于严重止吐剂疾病的患者。然而，长期操作带来了用户疲劳，从而降低了BCI系统在实际应用中的性能。疲劳机制的表征和SSVEP分类准确性的改进仍然是一个有挑战性的问题。在这封信中，我们首先进行SSVEP实验，以在正常和疲劳状态期间获得EEG信号。然后我们开发一种新颖的框架，它集成了多变量经验模式分解（MEMD）和支持向量机（SVM）的优点，以提高SSVEP分类精度，尤其是在疲劳状态期间。结果表明，与没有的方法相比，新颖的框架使我们能够获得更高的SSVEP分类准确性。 MEMD。此外，为了揭示疲劳的行为，我们使用多变量多尺度样本熵（MMSE）来分析与正常和疲劳状态相对应的多通道EEG信号。我们有趣地发现疲劳状态中的MMSE值低于正常状态的MMSE值，这反映了在疲劳状态期间SSVEP信号中规律性的增加。也就是说，当发生疲劳时，需要更大的神经组件同步来实现认知障碍。通过我们的分析获得了解基于SSVEP的BCI实验的脑疲劳行为的知识。版权所有（c）epla，2018

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  • 来源
    《EPL》 |2018年第4期|共7页
  • 作者

    Gao Zhong-Ke; Zhang Jun; Dang Wei-Dong; Yang Yu-Xuan; Cai Qing; Mu Chao-Xu; Grebogi Celso;

  • 作者单位

    Tianjin Univ Sch Elect &

    Informat Engn Tianjin 300072 Peoples R China;

    Tianjin Univ Sch Elect &

    Informat Engn Tianjin 300072 Peoples R China;

    Tianjin Univ Sch Elect &

    Informat Engn Tianjin 300072 Peoples R China;

    Tianjin Univ Sch Elect &

    Informat Engn Tianjin 300072 Peoples R China;

    Tianjin Univ Sch Elect &

    Informat Engn Tianjin 300072 Peoples R China;

    Tianjin Univ Sch Elect &

    Informat Engn Tianjin 300072 Peoples R China;

    Univ Aberdeen Inst Complex Syst &

    Math Biol Kings Coll Aberdeen AB24 3UE Scotland;

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  • 正文语种 eng
  • 中图分类 物理学;
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