论文标题
一种模型来自脑电图记录的意识水平的平均现场方法
A mean field approach to model levels of consciousness from EEG recordings
论文作者
论文摘要
我们介绍了一个平均场模型，用于分析人类意识的动态。特别是，受Giulio Tononi的综合信息理论的启发，以及最大tegmark的意识表示，我们研究了通过处理EEG信号生成的curie-weiss模型的订单disorder阶段过渡。后者已记录在接受深层镇静的健康个体上。然后，我们实施了一种机器学习工具，用于使用Curie-Weiss模型中计算的关键温度来对精神状态进行分类。结果表明，通过拟议的方法，可以区分意识状态和深层镇静状态。此外，我们确定了代表精神状态之间路径的状态空间，其尺寸对应于在脑电图信号的不同频带上计算出的临界温度。除了我们的模型导致的人类意识研究中可能具有理论意义，我们认为可以强调提出的方法可以用于临床应用。
We introduce a mean-field model for analysing the dynamics of human consciousness. In particular, inspired by the Giulio Tononi's Integrated Information Theory and by the Max Tegmark's representation of consciousness, we study order-disorder phase transitions on Curie-Weiss models generated by processing EEG signals. The latter have been recorded on healthy individuals undergoing deep sedation. Then, we implement a machine learning tool for classifying mental states using, as input, the critical temperatures computed in the Curie-Weiss models. Results show that, by the proposed method, it is possible to discriminate between states of awareness and states of deep sedation. Besides, we identify a state space for representing the path between mental states, whose dimensions correspond to critical temperatures computed over different frequency bands of the EEG signal. Beyond possible theoretical implications in the study of human consciousness, resulting from our model, we deem relevant to emphasise that the proposed method could be exploited for clinical applications.
