Linear Discriminant Analysis with Bayesian Risk Parameters for Myoelectric Control The linear discriminant analysis (LDA) classifier remains a standard in myoelectric control due to its simplicity, ease of implementation, and robustness. Despite this, challenges associated with the temporal evolution of the myoelectric signal may require flexibility beyond the capabilities of standard LDA. Recently proposed approaches have leveraged more complex systems, such as adaptive window framing or temporal convolutional neural networks to incorporate temporal structure. In this work, we explore the potential of exploiting parameters inherent to the LDA, which is conventionally applied assuming static and equal prior probabilities and uniform cost functions, to improve myoelectric control. First, a costmodified version of the LDA (cLDA) is introduced to better reflect the comparatively high cost of active errors. Second, an adaptive priors version of the LDA (pLDA) is introduced to reflect the changing prior probabilities of classes in the myoelectric signal time series. Results are compared against the standard LDA classifier using a novel dataset comprised of continuous class transitions. Although no significant differences were observed in total error, the proposed cLDA algorithm yielded significantly lower active error rates than the LDA alone. Furthermore, both the cLDA and pLDA classification schemes produced significantly lower instability than the LDA classifier alone, as measured by spurious changes in the output decision stream. This work lays the groundwork for future research on these flexible classification schemes including context dependent cost arrays, different methods of priors adaptation, and combinations of the proposed cLDA and pLDA frameworks.

Experiments with Random Projections Ensembles: Linear Versus Quadratic Discriminants Gaussian classifiers make the modelling assumption that each class follows a multivariate Gaussian distribution, and include the Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA). In high dimensional low sample size settings, a general drawback of these methods is that the sample covariance matrix may be singular, so their inverse is not available. Among many regularisation methods that attempt to get around this problem, the random projection ensemble approach for LDA has been both practically successful and theoretically well justified. The aim of this work is to investigate random projection ensembles for QDA, and to experimentally determine the relative strengths of LDA vs QDA in this context. We identify two different ways to construct averaging ensembles for QDA. The first is to aggregate low dimensional matrices to construct a smoothed estimate of the inverse covariance, which is then plugged into QDA. The second approach is to aggregate the predictions of low dimensional QDAs instead. By implementing experiments in eight different kinds of data sets, we conduct a thorough evaluation. From it we conclude that both QDA ensembles are able to improve on vanilla QDA, however the LDA ensemble tends to be more stable with respect to the choice of target dimension of the random projections.