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spatio-temporal features related to the activated areas. Aiming at the character-
istics of thinking centric functions, BI emphasizes on a
systematic
approach for
investigating human information processing mechanisms, including measuring,
collecting, modeling, transforming, managing, and mining multiple human brain
data obtained from various cognitive experiments by using fMRI (functional
magnetic resonance imaging) and EEG (electroencephalogram).
Such systematic study includes four core issues: systematic investigation of hu-
man thinking centric mechanisms, systematic design of cognitive experiments,
systematic human brain data management, and systematic human brain data
analysis and simulation. For supporting this systematic study, BI attempts to
capture new forms of collaborative and interdisciplinary work. New kinds of BI
methods and global research communities will emerge, through infrastructures
on the Wisdom Web[11] and knowledge grids, that enable high speed and dis-
tributed, agent-based large-scale analysis, simulations and computations, and
radically new ways of sharing data and knowledge.
A BI portal [12] is just such an infrastructure which implements data storage,
sharing, and utilization with a long-term, holistic vision for systematic data
management. It is oriented to analysis and simulation, so conceptual modeling of
brain data, i.e., Data-Brain modeling [2], is a core issue in BI portal construction.
Systematic BI methodology also supports such a Data-Brain construction.
This paper presents a case study on Data-Brain modeling based on BI method-
ology. The rest of this paper is organized as follows. Section 2 discusses back-
ground and related work. Section 3 gives the definition of Data-Brain and its
multi-dimension framework. Section 4 describes a BI methodology based ap-
proach for Data-Brain modeling. Finally, Section 5 gives concluding remarks.
2
Background and Related Work
As a typical “data” science, the key questions of recent Brain Informatics study
are how to obtain high quality of experimental data, how to manage such huge
multimedia data, as well as how to analyze such data for discovering new brain
related knowledge. Effective data management is the base of BI study. Over
the last decade, researchers have focused their efforts on constructing various
brain databases for supporting brain data management. These brain databases
can store microcosmic structure data [21] and macroscopic structure data [16],
respectively. As two kinds of important macroscopic brain data, both fMRI and
EEG are also focused. The researchers constructed many related brain databases,
such as fMRIDC [18], AED [15], all of which are oriented to data storage and
sharing.
The construction of traditional databases often adopts a top-down course,
from conceptual modeling to physical modeling. But in the existing studies of
brain databases, conceptual modeling of brain data is often neglected. Because
most of data in the existing brain databases come from separated studies, the
relationships among data are lack. Conceptual modeling can only be used to
describe the limited relationships among data in the same experiment task, so it
