Date of Award
Master of Science
Electrical and Computer Engineering
By modeling evoked potentials (EPs) as random vectors in which the EP samples are random variables, a unified strategy is introduced to determine multivariate central-tendency estimates such as the arithmetic mean, geometric mean, harmonic mean, median, tri-mean, trimmed-mean, and the Winsorized mean. Additionally, a generalized strategy is introduced to develop minimum-distance classifiers based on central-tendency estimates. Furthermore, procedures are developed to fuse the decisions of the resulting nearest-estimate classifiers for single-channel heterogeneous, multi-channel homogeneous, and multichannel heterogeneous-homogenous EP classification. The central-tendency estimates of real EPs are compared and it is shown that although the operations to compute the vector central-tendency estimates can be quite different, the EP estimates are similar with respect to their overall waveform shapes and peak latencies. It is also shown that by fusing homogeneous nearest-estimate classifier decisions across multiple channels, the classification accuracy can be improved significantly when compared with the accuracies of individual channel classifiers.
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