C4.5: programs for machine learning
C4.5: programs for machine learning
A Semi-Automatic Framework for Mining ERP Patterns
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 01
Development of NeuroElectroMagnetic ontologies(NEMO): a framework for mining brainwave ontologies
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Ontology Database: A New Method for Semantic Modeling and an Application to Brainwave Data
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part II
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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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 inMEG 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.