摘要:This paper describes a framework for automated classification and labeling of patterns in
electroencephalographic (EEG) and magnetoencephalographic (MEG) data. We describe recent progress
on four goals: 1) specification of rules and concepts that capture expert knowledge of event-related
potentials (ERP) patterns in visual word recognition; 2) implementation of rules in an automated data
processing and labeling stream; 3) data mining techniques that lead to refinement of rules; and 4) iterative
steps towards system evaluation and optimization. This process combines top-down, or knowledge-driven,
methods with bottom-up, or data-driven, methods. As illustrated here, these methods are complementary and
can lead to development of tools for pattern classification and labeling that are robust and conceptually
transparent to researchers. The present application focuses on patterns in averaged EEG (ERP) data. We also
describe efforts to extend our methods to represent patterns in MEG data, as well as EM patterns in source
(anatomical) space. The broader aim of this work is to design an ontology-based system to support
cross-laboratory, cross-paradigm, and cross-modal integration of brain functional data. Tools developed for
this project are implemented in MATLAB and are freely available on request.