Xi Yang, Zhang Lu, Li Cunzhen, Lv Xiaopeng, Lan Zhu
School of Computer Science, Northeast Electric Power University, Jilin, China.
Department of Chemoradiotherapy, Jilin City Hospital of Chemical Industry, Jilin, China.
Front Neurosci. 2025 Aug 7;19:1643554. doi: 10.3389/fnins.2025.1643554. eCollection 2025.
Audiovisual (AV) perception is a fundamental modality for environmental cognition and social communication, involving complex, non-linear multisensory processing of large-scale neuronal activity modulated by attention. However, precise characterization of the underlying AV processing dynamics remains elusive.
We designed an AV semantic discrimination task to acquire electroencephalogram (EEG) data under attended and unattended conditions. To temporally resolve the neural processing stages, we developed an EEG microstate-based analysis method. This involved segmenting the EEG into functional sub-stages by applying hierarchical clustering to global field power-peak topographic maps. The optimal number of microstate classes was determined using the Krzanowski-Lai criterion and Global Explained Variance evaluation. We analyzed filtered EEG data across frequency bands to quantify microstate attributes (e.g., duration, occurrence, coverage, transition probabilities), deriving comprehensive time-frequency features. These features were then used to classify processing states with multiple machine learning models.
Distinct, temporally continuous microstate sequences were identified characterizing attended versus unattended AV processing. The analysis of microstate attributes yielded time-frequency features that achieved high classification accuracy: 97.8% for distinguishing attended vs. unattended states and 98.6% for discriminating unimodal (auditory or visual) versus multimodal (AV) processing across the employed machine learning models.
Our EEG microstate-based method effectively characterizes the spatio-temporal dynamics of AV processing. Furthermore, it provides neurophysiologically interpretable explanations for the highly accurate classification outcomes, offering significant insights into the neural mechanisms underlying attended and unattended multisensory integration.
视听（AV）感知是环境认知和社会交流的一种基本模态，涉及由注意力调制的大规模神经元活动的复杂、非线性多感官处理。然而，潜在的AV处理动力学的精确表征仍然难以捉摸。
我们设计了一项AV语义辨别任务，以获取在有注意力和无注意力条件下的脑电图（EEG）数据。为了在时间上解析神经处理阶段，我们开发了一种基于EEG微状态的分析方法。这包括通过对全局场功率峰值地形图应用层次聚类，将EEG分割为功能子阶段。使用Krzanowski-Lai准则和全局解释方差评估来确定微状态类的最佳数量。我们分析了跨频段的滤波后EEG数据，以量化微状态属性（例如，持续时间、出现次数、覆盖范围、转换概率），得出全面的时频特征。然后使用这些特征通过多种机器学习模型对处理状态进行分类。
识别出了不同的、时间上连续的微状态序列，表征了有注意力和无注意力的AV处理。对微状态属性的分析产生了时频特征，这些特征实现了高分类准确率：在所采用的机器学习模型中，区分有注意力和无注意力状态的准确率为97.8%，区分单模态（听觉或视觉）与多模态（AV）处理的准确率为98.6%。
我们基于EEG微状态的方法有效地表征了AV处理的时空动力学。此外，它为高度准确的分类结果提供了神经生理学上可解释的解释，为有注意力和无注意力多感官整合背后的神经机制提供了重要见解。
