A Semi-Automatic Framework for Mining ERP Patterns

Authors:
Jiawei Rong;Dejing Dou;Gwen Frishkoff;Robert Frank;Allen Malony;Don Tucker
Affiliations:
University of Oregon, USA;University of Oregon, USA;University of Pittsburgh, USA;University of Oregon, USA;University of Oregon, USA;University of Oregon, USA
Venue:
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 01
Year:
2007

Citing 0
Cited 2

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
A framework to support automated classification and labeling of brain electromagnetic patterns

Computational Intelligence and Neuroscience - Regular issue

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Abstract

Event-related potentials (ERP) are brain electrophysiological patterns created by averaging electroencephalographic (EEG) data, time-locking to events of interest (e.g., stimulus or response onset). In this paper, we propose a semi-automatic framework for mining ERP data, which includes the following steps: PCA decomposition, extraction of summary metrics, unsupervised learning (clustering) of patterns, and supervised learning, i.e. discovery, of classification rules. Results show good correspondence between rules that emerge from decision tree classifiers and rules that were independently derived by domain experts. In addition, data mining results suggested ways in which expert-defined rules might be refined to improve pattern representation and classification results.