Martínez-Cagigal Víctor, Santamaría-Vázquez Eduardo, Hornero Roberto
Biomedical Engineering Group, E.T.S.I. Telecomunicación, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain.
Entropy (Basel). 2019 Feb 27;21(3):230. doi: 10.3390/e21030230.
Brain-computer interfaces (BCI) have traditionally worked using synchronous paradigms. In recent years, much effort has been put into reaching asynchronous management, providing users with the ability to decide when a command should be selected. However, to the best of our knowledge, entropy metrics have not yet been explored. The present study has a twofold purpose: (i) to characterize both control and non-control states by examining the regularity of electroencephalography (EEG) signals; and (ii) to assess the efficacy of a scaled version of the sample entropy algorithm to provide asynchronous control for BCI systems. Ten healthy subjects participated in the study, who were asked to spell words through a visual oddball-based paradigm, attending (i.e., control) and ignoring (i.e., non-control) the stimuli. An optimization stage was performed for determining a common combination of hyperparameters for all subjects. Afterwards, these values were used to discern between both states using a linear classifier. Results show that control signals are more complex and irregular than non-control ones, reaching an average accuracy of 94 . 40 % in classification. In conclusion, the present study demonstrates that the proposed framework is useful in monitoring the attention of a user, and granting the asynchrony of the BCI system.
脑机接口（BCI）传统上采用同步范式工作。近年来，人们在实现异步管理方面投入了大量精力，为用户提供决定何时选择命令的能力。然而，据我们所知，熵度量尚未得到探索。本研究有两个目的：（i）通过检查脑电图（EEG）信号的规律性来表征控制状态和非控制状态；（ii）评估样本熵算法的缩放版本为BCI系统提供异步控制的有效性。10名健康受试者参与了该研究，他们被要求通过基于视觉oddball的范式拼写单词，关注（即控制）和忽略（即非控制）刺激。进行了一个优化阶段，以确定所有受试者超参数的共同组合。之后，使用线性分类器利用这些值来区分两种状态。结果表明，控制信号比非控制信号更复杂、更不规则，分类平均准确率达到94.40%。总之，本研究表明所提出的框架在监测用户注意力和实现BCI系统异步方面是有用的。
