Fine-Grained Implicit Intention Pattern Recognition for Key Interactive Tasks in Industrial Human–Machine Collaboration

The information symmetry between humans and machines can enhance mutual perception and understanding, leading to more robust cooperation. Intention recognition is a key technology in natural human–machine collaboration (HMC). However, as the complexity of the system increases, the amount of information and the types of tasks become numerous, which leads to continuous dynamic changes in interaction intentions. New sensing technologies such as electroencephalogram (EEG) have provided a continuous and unobtrusive monitoring approach to accurately and effectively identify intentions. But the complexity of physiological responses and the uncertain nature of intention make cross-subject recognition difficult, resulting in poor generalization performance and coarse-grained recognition patterns. To address these limitations, we proposed a framework for modeling tasks in complex systems and applied it to model tasks in an industrial system. Then, we use operators’ EEG data to effectively recognize the fine-grained intention patterns within different typical task scenarios, such as monitoring production and communication tasks. By inputting the improved multi-channel phase synchronization features into a machine learning classifier, cross-subject accuracy rates of 99.42% and 99.91% were achieved. This work furnishes systematic, field-tested cases for task modeling in the industrial field, demonstrates high-performance implicit intention recognition with a single EEG modality, and refines the granularity of implicit intention recognition. It provides theoretical underpinnings and technical support for both human–machine information symmetry and the advancement of HMC hybrid intelligence.