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.

中文翻译：

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从 EEG 记录模拟意识水平的平均场方法

我们引入了一个平均场模型来分析人类意识的动态。特别是，受 Giulio Tononi 的综合信息理论和 Max Tegmark 的意识表征的启发，我们研究了通过处理 EEG 信号生成的 Curie-Weiss 模型的有序-无序相变。后者已被记录在接受深度镇静的健康个体身上。然后，我们实现了一个机器学习工具，用于使用在 Curie-Weiss 模型中计算的临界温度作为输入来对心理状态进行分类。结果表明，通过所提出的方法，可以区分意识状态和深度镇静状态。此外，我们确定了一个状态空间来表示心理状态之间的路径，其维度对应于在 EEG 信号的不同频带上计算的临界温度。除了由我们的模型产生的人类意识研究中可能的理论意义之外，我们认为相关地强调所提出的方法可以用于临床应用。