Faculty of Mechanical Engineering, Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil.
Potsdam Institute for Climate Impact Research, P.O. Box 601203, 14412, Potsdam, Germany.
Sci Rep. 2020 Dec 4;10(1):21241. doi: 10.1038/s41598-020-77903-4.
In this paper, we present the new frequency spectrum recurrence analysis technique by means of electro-encephalon signals (EES) analyses. The technique is suitable for time series analysis with noise and disturbances. EES were collected, and alpha waves of the occipital region were analysed by comparing the signals from participants in two states, eyes open and eyes closed. Firstly, EES were characterized and analysed by means of techniques already known to compare with the results of the innovative technique that we present here. We verified that, standard recurrence quantification analysis by means of EES time series cannot statistically distinguish the two states. However, the new frequency spectrum recurrence quantification exhibit quantitatively whether the participants have their eyes open or closed. In sequence, new quantifiers are created for analysing the recurrence concentration on frequency bands. These analyses show that EES with similar frequency spectrum have different recurrence levels revealing different behaviours of the nervous system. The technique can be used to deepen the study on depression, stress, concentration level and other neurological issues and also can be used in any complex system.
本文通过脑电信号(EES)分析提出了新的频谱递归分析技术。该技术适用于具有噪声和干扰的时间序列分析。我们采集了脑电信号,并通过比较睁眼和闭眼两种状态下参与者的信号,分析了枕部的阿尔法波。首先,我们使用已经熟知的技术对脑电信号进行了特征提取和分析,并将结果与我们提出的创新技术进行了比较。我们验证了,通过 EES 时间序列进行的标准递归量化分析无法从统计学上区分这两种状态。然而,新的频谱递归量化分析可以定量地判断参与者是睁眼还是闭眼。接下来,我们为分析频带的递归集中创建了新的量化指标。这些分析表明,具有相似频谱的脑电信号具有不同的递归水平,揭示了神经系统的不同行为。该技术可用于深化对抑郁、压力、注意力水平和其他神经问题的研究,也可用于任何复杂系统。