This study was based on the contemporary proposal that distinct states of consciousness are quantifiable by neural complexity and critical dynamics. To test this hypothesis, it was aimed at comparing the electrophysiological correlates of three meditation conditions using nonlinear techniques from the complexity and criticality framework as well as power spectral density. Thirty participants highly proficient in meditation were measured with 64-channel electroencephalography (EEG) during one session consisting of a task-free baseline resting (eyes closed and eyes open), a reading condition, and three meditation conditions (thoughtless emptiness, presence monitoring, and focused attention). The data were analyzed applying analytical tools from criticality theory (detrended fluctuation analysis, neuronal avalanche analysis), complexity measures (multiscale entropy, Higuchi’s fractal dimension), and power spectral density. Task conditions were contrasted, and effect sizes were compared. Partial least square regression and receiver operating characteristics analysis were applied to determine the discrimination accuracy of each measure. Compared to resting with eyes closed, the meditation categories emptiness and focused attention showed higher values of entropy and fractal dimension. Long-range temporal correlations were declined in all meditation conditions. The critical exponent yielded the lowest values for focused attention and reading. The highest discrimination accuracy was found for the gamma band (0.83–0.98), the global power spectral density (0.78–0.96), and the sample entropy (0.86–0.90). Electrophysiological correlates of distinct meditation states were identified and the relationship between nonlinear complexity, critical brain dynamics, and spectral features was determined. The meditation states could be discriminated with nonlinear measures and quantified by the degree of neuronal complexity, long-range temporal correlations, and power law distributions in neuronal avalanches.

中文翻译：

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根据频谱、复杂性和关键性特征确定脑电图中的意识状态


这项研究基于当代的提议，即不同的意识状态可以通过神经复杂性和批判动力学来量化。为了检验这一假设，目的是使用复杂性和临界性框架以及功率谱密度的非线性技术来比较三种冥想条件的电生理相关性。 30 名高度精通冥想的参与者在一次会议中接受了 64 通道脑电图 (EEG) 测量，包括无任务基线休息（闭眼和睁眼）、阅读条件和三种冥想条件（无意识的空虚、存在监测、并集中注意力）。使用临界性理论（去趋势波动分析、神经元雪崩分析）、复杂性度量（多尺度熵、Higuchi 分形维数）和功率谱密度的分析工具对数据进行分析。对比任务条件并比较效果大小。应用偏最小二乘回归和接收者操作特征分析来确定每个测量的辨别准确性。与闭眼休息相比，冥想类别空虚和集中注意力表现出更高的熵值和分形维数。在所有冥想条件下，长程时间相关性均下降。关键指数产生了集中注意力和阅读的最低值。伽玛波段 (0.83–0.98)、全局功率谱密度 (0.78–0.96) 和样本熵 (0.86–0.90) 的辨别精度最高。 确定了不同冥想状态的电生理相关性，并确定了非线性复杂性、关键大脑动力学和光谱特征之间的关系。冥想状态可以通过非线性测量来区分，并通过神经元复杂程度、长期时间相关性和神经元雪崩的幂律分布来量化。