IntroductionMicrostate analysis enables the characterization of quasi-stable scalp potential fields on a sub-second timescale, preserving the temporal dynamics of EEG and spatial information of scalp potential distributions. Owing to its capacity to provide comprehensive pathological insights, it has been widely applied in the investigation of schizophrenia (SCZ). Nevertheless, previous research has primarily concentrated on differences in individual microstate temporal characteristics, neglecting potential distinctions in microstate semantic sequences and not fully considering the issue of the universality of microstate templates between SCZ patients and healthy individuals.MethodsThis study introduced a microstate semantic modeling analysis method aimed at schizophrenia recognition. Firstly, microstate templates corresponding to both SCZ patients and healthy individuals were extracted from resting-state EEG data. The introduction of a dual-template strategy makes a difference in the quality of microstate sequences. Quality features of microstate sequences were then extracted from four dimensions: Correlation, Explanation, Residual, and Dispersion. Subsequently, the concept of microstate semantic features was proposed, decomposing the microstate sequence into continuous sub-sequences. Specific semantic sub-sequences were identified by comparing the time parameters of sub-sequences.ResultsThe SCZ recognition test was performed on the public dataset for both the quality features and semantic features of microstate sequences, yielding an impressive accuracy of 97.2%. Furthermore, cross-subject experimental validation was conducted, demonstrating that the method proposed in this paper achieves a recognition rate of 96.4% between different subjects.DiscussionThis research offers valuable insights for the clinical diagnosis of schizophrenia. In the future, further studies will seek to augment the sample size to enhance the effectiveness and reliability of this method.

中文翻译：

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基于微状态语义建模方法的精神分裂症患者脑电图分析

简介微状态分析能够在亚秒级时间尺度上表征准稳定的头皮电位场，保留脑电图的时间动态和头皮电位分布的空间信息。由于其能够提供全面的病理学见解，它已广泛应用于精神分裂症（SCZ）的研究。然而，以往的研究主要集中在个体微状态时间特征的差异上，忽略了微状态语义序列的潜在差异，没有充分考虑SCZ患者和健康个体之间微状态模板的普适性问题。方法本研究引入了微状态语义建模分析方法旨在识别精神分裂症。首先，从静息态脑电图数据中提取与 SCZ 患者和健康个体相对应的微状态模板。双模板策略的引入使微状态序列的质量发生了变化。然后从四个维度提取微观状态序列的质量特征：相关性、解释性、残差性和离散性。随后，提出了微状态语义特征的概念，将微状态序列分解为连续的子序列。通过比较子序列的时间参数来识别特定的语义子序列。结果在公共数据集上对微态序列的质量特征和语义特征进行了SCZ识别测试，准确率高达97.2%。此外，还进行了跨受试者实验验证，表明本文提出的方法在不同受试者之间的识别率达到了96.4%。讨论本研究为精神分裂症的临床诊断提供了有价值的见解。未来，进一步的研究将寻求扩大样本量，以提高该方法的有效性和可靠性。