Wu Shang-Ju, Nicolaou Nicoletta, Bogdan Martin
Neuromorphic Information Processing, Leipzig University, Augustusplatz 10, 04109 Leipzig, Germany.
Department of Basic and Clinical Sciences, University of Nicosia Medical School, 93 Agiou Nikolaou Street, Engomi 2408, Nicosia, Cyprus.
Entropy (Basel). 2020 Dec 15;22(12):1411. doi: 10.3390/e22121411.
Completely locked-in state (CLIS) patients are unable to speak and have lost all muscle movement. From the external view, the internal brain activity of such patients cannot be easily perceived, but CLIS patients are considered to still be conscious and cognitively active. Detecting the current state of consciousness of CLIS patients is non-trivial, and it is difficult to ascertain whether CLIS patients are conscious or not. Thus, it is important to find alternative ways to re-establish communication with these patients during periods of awareness, and one such alternative is through a brain-computer interface (BCI). In this study, multiscale-based methods (multiscale sample entropy, multiscale permutation entropy and multiscale Poincaré plots) were applied to analyze electrocorticogram signals from a CLIS patient to detect the underlying consciousness level. Results from these different methods converge to a specific period of awareness of the CLIS patient in question, coinciding with the period during which the CLIS patient is recorded to have communicated with an experimenter. The aim of the investigation is to propose a methodology that could be used to create reliable communication with CLIS patients.
完全闭锁状态（CLIS）患者无法说话且丧失了所有肌肉运动能力。从外部来看，这类患者的大脑内部活动不易被察觉，但CLIS患者被认为仍有意识且认知活跃。检测CLIS患者当前的意识状态并非易事，很难确定CLIS患者是否有意识。因此，重要的是找到在患者有意识期间与这些患者重新建立沟通的替代方法，其中一种替代方法是通过脑机接口（BCI）。在本研究中，基于多尺度的方法（多尺度样本熵、多尺度排列熵和多尺度庞加莱图）被应用于分析一名CLIS患者的皮层脑电图信号，以检测潜在的意识水平。这些不同方法的结果都指向了该CLIS患者的一个特定意识期，这与记录到该CLIS患者与实验者进行交流的时期相吻合。该研究的目的是提出一种可用于与CLIS患者建立可靠沟通的方法。
