Graph-based Recurrence Quantification Analysis of EEG Spectral Dynamics for Motor Imagery-based BCIs.
Graph-based Recurrence Quantification Analysis of EEG Spectral Dynamics for Motor Imagery-based BCIs.
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基于运动想象的 BCI 的脑电图频谱动力学的基于图形的递归量化分析。
DOI：
10.1109/embc46164.2021.9630068
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Shahriari,Yalda
中科院分区：
文献类型：
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作者：
Hosni,SarahMIsmail;Borgheai,SeyyedBahram;McLinden,John;Zhu,Shaotong;Huang,Xiaofei;Ostadabbas,Sarah;Shahriari,Yalda
尽管不断的研究，基于脑机接口（BCI）的通信方法还没有一个有效的和可靠的手段，严重残疾患者可以依靠，到目前为止，大多数基于运动想象（MI）的BCI系统使用传统的频谱分析方法来提取判别特征和分类相关的脑电图（EEG）为基础的感觉运动节律（SMR）的动态，导致相对较低的性能。在这项研究中，我们研究了使用递归量化分析（RQA）和复杂网络理论基于图的特征提取方法作为一种新的方法来提高MI-BCI性能的可行性。植根于混沌理论，这些功能探索的非线性动力学的MI神经反应作为一个新的信息维度在分类MI.MethodEEG时间序列记录从6个健康参与者执行MI休息任务被投射到多维相空间轨迹，以构建相应的复发图（RP）。从RP中提取了8个基于非线性图的RQA特征，然后使用线性支持向量机（SVM）分类器通过5重嵌套交叉验证程序进行参数优化，将其与经典光谱特征进行比较。这些发现表明，RQA和复杂网络分析可以代表新的信息维度的非线性特征的EEG信号，以提高MI-BCI的性能。
Despite continuous research, communication approaches based on brain-computer interfaces (BCIs) are not yet an efficient and reliable means that severely disabled patients can rely on. To date, most motor imagery (MI)-based BCI systems use conventional spectral analysis methods to extract discriminative features and classify the associated electroencephalogram (EEG)-based sensorimotor rhythms (SMR) dynamics that results in relatively low performance. In this study, we investigated the feasibility of using recurrence quantification analysis (RQA) and complex network theory graph-based feature extraction methods as a novel way to improve MI-BCIs performance. Rooted in chaos theory, these features explore the nonlinear dynamics underlying the MI neural responses as a new informative dimension in classifying MI.MethodEEG time series recorded from six healthy participants performing MI-Rest tasks were projected into multidimensional phase space trajectories in order to construct the corresponding recurrence plots (RPs). Eight nonlinear graph-based RQA features were extracted from the RPs then compared to the classical spectral features through a 5-fold nested cross-validation procedure for parameter optimization using a linear support vector machine (SVM) classifier.ResultsNonlinear graph-based RQA features were able to improve the average performance of MI-BCI by 5.8% as compared to the classical features.SignificanceThese findings suggest that RQA and complex network analysis could represent new informative dimensions for nonlinear characteristics of EEG signals in order to enhance the MI-BCI performance.