A dynamic channel selection strategy for dense-array ERP classification.

The goal of this paper is to introduce a new strategy to accurately classify event-related potentials (ERPs), recorded using dense electrode arrays, into predefined brain activity categories. The challenge is to exploit the enhanced spatial information offered by dense arrays while overcoming the significant increase in the dimensionality problem introduced by the large increase in the number of channels. These conflicting objectives are achieved by introducing a spatiotemporal-array model to observe the dense-array ERP amplitude variations across channels and time, simultaneously. To account for latency variations and EEG noise in the array elements, each spatiotemporal element in the array is initially modeled as a Gaussian random variable. A two-step process that uses the Kolmogrov-Smirnov test and the Lilliefors test is formulated to select the array elements that have different Gaussian densities across all ERP categories. Selecting spatiotemporal elements that fit the assumed model and also statistically differ across the ERP categories not only ensures high classification accuracies but also decreases the dimensionality significantly. The selection is dynamic in the sense that selecting spatiotemporal-array elements corresponds to selecting ERP samples of different channels at different time instants. Each selected array element is classified using a univariate Gaussian classifier, and the resulting decisions are fused into a decision fusion vector that is classified using a discrete Bayes classifier. By converting an inherently multivariate classification problem into a simpler problem involving only univariate classifications, the dimensionality problem that plagues the design of practical multivariate ERP classifiers is circumvented. Consequently, classifiers can be designed to classify the ERPs that are unique to an individual without having to collect a prohibitively large ERP dataset from him/her. The application of the resulting dynamic-channel-selection-based classification strategy is demonstrated by designing and testing classifiers for eight subjects using ERPs from a Stroop color test and it is shown that the strategy yields high classification accuracies. Finally, it is noted that because of the generalized formulation of the strategy, it can be applied to various other problems involving the classification of multivariate signals acquired from multiple identical or multiple heterogeneous sensors.