期刊
2020 IEEE 17TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2020)
卷 -, 期 -, 页码 271-274出版社
IEEE
DOI: 10.1109/isbi45749.2020.9098708
关键词
EEG; Phase locking value; Brain functional network; Emotion; Classification
资金
- Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) - Ministry of Health & Welfare, Republic of Korea [HI18C1038]
Network perspective studies of the human brain are rapidly increasing due to the advances in the field of network neuroscience. In several brain network based applications, recognition of event-associated brain functional networks (BFNs) can be crucial to understand the event processing in the brain and can play a significant role to characterize and quantify the complex brain networks. This paper presents a framework to identify the event-associated BFNs using phase locking value (PLV) in EEG. Based on the PLV dissimilarities during the rest and event tasks, we identify the reactive band and the event-associated most reactive pairs (MRPs). With the MRPs identified, the event-associated BFNs are formed. The proposed method is employed on `database for emotion analysis using physiological signals (DEAP)' data set to form the BFNs associated with several emotions. With the emotion-associated BFNs as features, comparable state-of-the-art multiple emotion classification accuracies are achieved. Results show that, with the proposed method, event-associated BFNs can be identified and can be used in brain network based applications.
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