Performance monitoring for brain-computer-interface actions

When presented with a difficult perceptual decision, human observers are able to make metacognitive judgements of subjective certainty. Such judgements can be made independently of and prior to any overt response to a sensory stimulus, presumably via internal monitoring. Retrospective judgements about one's own task performance, on the other hand, require first that the subject perform a task and thus could potentially be made based on motor processes, proprioceptive, and other sensory feedback rather than internal monitoring. With this dichotomy in mind, we set out to study performance monitoring using a brain-computer interface (BCI), with which subjects could voluntarily perform an action moving a cursor on a computer screen - without any movement of the body, and thus without somatosensory feedback. Real-time visual feedback was available to subjects during training, but not during the experiment where the true final position of the cursor was only revealed after the subject had estimated where s/he thought it had ended up after 6 s of BCI-based cursor control. During the first half of the experiment subjects based their assessments primarily on the prior probability of the end position of the cursor on previous trials. However, during the second half of the experiment subjects' judgements moved significantly closer to the true end position of the cursor, and away from the prior. This suggests that subjects can monitor task performance when the task is performed without overt movement of the body. (C) 2016 Elsevier Inc. All rights reserved.

Ant colony optimization for the single model U-type assembly line balancing problem

Arda Alp

An assembly line is a production line in which units move continuously through a sequence of stations. The assembly line balancing problem is defined as the allocation of tasks to an ordered sequence of stations subject to precedence constraints with the objective of optimizing a performance measure. In this paper, we propose ant colony algorithms to solve the single-model U-type assembly line balancing problem. We conduct an extensive experimental study in which the performance of the proposed algorithm is compared against best known algorithms reported in the literature. The results indicate that the proposed algorithms display very competitive performance against them. (C) 2009 Elsevier B.V. All rights reserved.

2009

Morphology-driven automatic segmentation of MR images of the neonatal brain

Laura Ioana Gui, Michel Kocher, François Lazeyras, Radoslaw Lisowski

The segmentation of MR images of the neonatal brain is an essential step in the study and evaluation of infant brain development. State-of-the-art methods for adult brain MRI segmentation are not applicable to the neonatal brain, due to large differences in structure and tissue properties between newborn and adult brains. Existing newborn brain MRI segmentation methods either rely on manual interaction or require the use of atlases or templates, which unavoidably introduces a bias of the results towards the population that was used to derive the atlases. We propose a different approach for the segmentation of neonatal brain MRI, based on the infusion of high-level brain morphology knowledge, regarding relative tissue location, connectivity and structure. Our method does not require manual interaction, or the use of an atlas, and the generality of its priors makes it applicable to different neonatal populations, while avoiding atlas-related bias. The proposed algorithm segments the brain both globally (intracranial cavity, cerebellum, brainstem and the two hemispheres) and at tissue level (cortical and subcortical gray matter, myelinated and unmyelinated white matter, and cerebrospinal fluid). We validate our algorithm through visual inspection by medical experts, as well as by quantitative comparisons that demonstrate good agreement with expert manual segmentations. The algorithm's robustness is verified by testing on variable quality images acquired on different machines, and on subjects with variable anatomy (enlarged ventricles, preterm- vs. term-born). (C) 2012 Elsevier B.V. All rights reserved.

Elsevier Science Bv2012

Interactive Learning from Unlabeled Instructions

Inaki Asier Iturrate Gil

Interactive learning deals with the problem of learning and solving tasks using human instructions. It is common in human-robot interaction, tutoring systems, and in human-computer interfaces such as brain-computer ones. In most cases, learning these tasks is possible because the signals are predefined or an ad-hoc calibration procedure allows to map signals to specific meanings. In this paper, we address the problem of simultaneously solving a task under human feedback and learning the associated meanings of the feedback signals. This has important practical application since the user can start controlling a device from scratch, without the need of an expert to define the meaning of signals or carrying out a calibration phase. The paper proposes an algorithm that simultaneously assign meanings to signals while solving a sequential task under the assumption that both, human and machine, share the same a priori on the possible instruction meanings and the possible tasks. Furthermore, we show using synthetic and real EEG data from a brain-computer interface that taking into account the uncertainty of the task and the signal is necessary for the machine to actively plan how to solve the task efficiently.

2014