Hierarchical Self-organizing Maps of NIRS and EEG Signals for Recognition of Brain States

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• 关键词：Brain state ; NIRS ; EEG ; Self ; organizing map ; DIKW model
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9677
• 期：1
• 页码：335-344
• 全文大小：502 KB
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• 作者单位：Katsunori Oyama (19)
  Kaoru Sakatani (20)
  Hua Ming (21)
  Carl K. Chang (22)
  
  19. Department of Computer Science, Nihon University, Koriyama, Japan
  20. Department of Electrical and Electronics Engineering, Nihon University, Koriyama, Japan
  21. Department of Computer Science and Engineering, Oakland University, Rochester, USA
  22. Department of Computer Science, Iowa State University, Ames, USA
  
• 丛书名：Inclusive Smart Cities and Digital Health
• ISBN：978-3-319-39601-9
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9677

文摘

Recent advances in temporal data mining of brain activity with NIRS and EEG signals allow us to recognize brain states in higher resolution. However, brain states are not always distinct from each other and often differ in temporal granularity. This paper revisits Dennett’s three levels of stance, the DIKW model for the design of two self-organizing maps (SOMs), which contributes to recognition of a hierarchy of brain states with finer granularities. The experimental results show that two brain states at different levels can be accurately identified by applying different training data for each level of SOM.