Human-computer interaction multi-task modeling based on implicit intent EEG decoding

In the short term with fully autonomous level of machine intelligence can not be achieved,human are still an important part of the Human Computer Interaction (HCI) systems.Intelligent systems should be able to "feel" and "predict" human intentions to achieve multi-channel natural and dynamic collaboration between human and machine,which is very important to improve the safety and efficiency of HCI system.However,the process of HCI is filled with a lot of fuzzy and hidden implicit intentions,and the analysis efficiency and accuracy using traditional psychological or behavioral analysis methods are poor.With the development of sensing technology,the intention recognition based on physiological signals has become the main method,but the existing research basis has some problems,such as few separable modes,low recognition accuracy and insufficient research on domain context.A method to integrate human into human-machine coordination loop naturally based on passive brain-computer interface technology was presented in the field of industrial control complex system.The typical interactive tasks of Human-Machine Interaction (HMI) were extracted based on Multi-Attribute Task Battery (MATB) multi-task paradigm.Then,the common space pattern algorithm was used to extract the Electroencephalogram(EEG) spatial features of multi-task intention.A machine learning intention model was constructed to decode implicit intention EEG and realize automatic recognition of implicit intention through subject-wise cross-validation and 5-fold parameter optimization.It was proved that EEG signals could be used as the basis for judging the implicit intention of HCI,and CSP+SVM algorithm model could effectively improve the EEG decoding performance of implicit intention.The translation of implicit intent information was of significance for the study of intent-based efficient HCI model,the development of HCI systems and the improvement of human-machine collaboration efficiency. © 2024 CIMS. All rights reserved.