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BUILDING A DATA‐MINING GRID FOR MULTIPLE HUMAN BRAIN DATA ANALYSIS
Author(s) -
Zhong Ning,
Hu Jia,
Motomura Shinichi,
Wu JingLong,
Liu Chunnian
Publication year - 2005
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.0824-7935.2005.00270.x
Subject(s) - computer science , data science , informatics , perspective (graphical) , cognition , grid , key (lock) , field (mathematics) , knowledge extraction , artificial intelligence , data mining , psychology , engineering , geometry , mathematics , computer security , neuroscience , pure mathematics , electrical engineering
E‐science is about global collaboration in key areas of science such as cognitive science and brain science, and the next generation of infrastructure such as the Wisdom Web and Knowledge Grids. As a case study, we investigate human multiperception mechanism by cooperatively using various psychological experiments, physiological measurements, and data mining techniques for developing artificial systems which match human ability in specific aspects. In particular, we observe fMRI (functional magnetic resonance imaging) and EEG (electroencephalogram) brain activations from the viewpoint of peculiarity oriented mining and propose a way of peculiarity oriented mining for knowledge discovery in multiple human brain data. Based on such experience and needs, we concentrate on the architectural aspect of a brain‐informatics portal from the perspective of the Wisdom Web and Knowledge Grids. We describe how to build a data‐mining grid on the Wisdom Web for multiaspect human brain data analysis. The proposed methodology attempts to change the perspective of cognitive scientists from a single type of experimental data analysis toward a holistic view at a long‐term, global field of vision.