Abstract-Several studies describe evoked EEG potentials elicited when a subject is aware of an erroneous decision either taken by him or by an external interface. This paper try to detect Error-related potentials (ErrP) elicited when a human user want to monitors an external system upon which he has no control whatsoever. To this end we use a Bayesian filter to classify erroneous or correct events. On average over three subjects, the proposed probabilistic classifier achieves single-trial classification of 85% for correct trials and 71% for erroneous trials.
International audienceMany pull policies can be found in the literature for controlling multistage production/inventory systems. In this paper, we present a framework that enables us to describe the dynamics of a large class of pull control policies, using the same set of canonical functions. The class of policies we consider here include well known pull policies, like kanban, CONWIP, basestock, generalized kanban, extended kanban, but also many other hybrid policies, and their extensions to systems producing batches. Each of these policies is characterized by the values of some parameters. These parameters values are calculated thanks to a computational algorithm that relies on the use of path algebra tools, especially (min,+) algebra tools. This canonical formulation allows to identify under which values of the control parameters, two different policies have the same dynamics behavior. It also enables to derive methods for evaluating and comparing the performance of several pull control policies, as we illustrate it in the paper
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