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

Abstract

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.

Figure 1

(a) Time course of P100 pattern, plotted at left occipital electrode, O1. Time is plotted on the $x$-axis (0–700 milliseconds); each vertical hash mark represents 100 milliseconds. Amplitude is plotted on the $y$-axis (scale, $\pm 4$  $\mu$V). The dark vertical line marks the time of peak amplitude (∼120 milliseconds). (b) Scalp topography of the P100 pattern, plotted at the time of peak amplitude. Red, positive. Blue, negative.

Figure 2

Pattern classification and labeling scheme. Knowledge engineering (processes 1, 2) includes “top-down” specification of ERP concepts and rules, formulated by domain experts. Component analysis and measure generation (processes 3, 4) yield summary metrics that are used for pattern classification and labeling. Implementation and operationalization of pattern rules (processes 5, 6) are detailed in Section 2. Data mining (processes 7, 8) includes “bottom-up” or data-driven methods for clustering and discovery of pattern rules (Section 5). System evaluation is detailed in Section 4.

Figure 3

Autoclassification and labeling results. (a) Percentage of observations matching rule criteria for each pattern. (b) Topogragraphy and (c) time course of pattern factors.