面向脑电数据的知识建模和情感识别

Psychological research relies on physical and psychological data, and emotion has always been an important subject in the field of psychology. Recently, with the development of cognitive neuroscience technology, researchers can study topics such as emotion recognition and the emotional brain using electroencephalogram (EEG) and other physiological signals that reflect brain activity. These physiological signals can generate terabytes or even petabytes of data, and in fact, cognitive research and clinical neuroscience has already accumulated a wealth of data over the past several decades. Knowledge representation of this large amount of data and mining it for information regarding emotion requires more advanced tools. Making a data model that can clearly represent the meaning of data associated with emotion information would create a knowledge-sharing platform for psychological researchers to access the vast amount of data for further scientific research related to emotion. This paper provides such an ontology model that represents the semantics of EEG data with contextual information about the subjects. We used the model in conjunction with a reasoning engine to perform automatic emotion recognition based on EEG signals. Experimental results show that the ontology model reaches an average accuracy of 99.11% in identifying the emotional state of the subjects. Analysis of the results suggests that the most critical characteristic of EEG-based emotion recognition is the absolute power ratio between beta and theta waves.