EEG-based cognitive control behaviour assessment: an ecological study with professional air traffic controllers

Several models defining different types of cognitive human behaviour are available. For this work, we have selected the Skill, Rule and Knowledge (SRK) model proposed by Rasmussen in 1983. This model is currently broadly used in safety critical domains, such as the aviation. Nowadays, there are no tools able to assess at which level of cognitive control the operator is dealing with the considered task, that is if he/she is performing the task as an automated routine (skill level), as procedures-based activity (rule level), or as a problem-solving process (knowledge level). Several studies tried to model the SRK behaviours from a Human Factor perspective. Despite such studies, there are no evidences in which such behaviours have been evaluated from a neurophysiological point of view, for example, by considering brain activity variations across the different SRK levels. Therefore, the proposed study aimed to investigate the use of neurophysiological signals to assess the cognitive control behaviours accordingly to the SRK taxonomy. The results of the study, performed on 37 professional Air Traffic Controllers, demonstrated that specific brain features could characterize and discriminate the different SRK levels, therefore enabling an objective assessment of the degree of cognitive control behaviours in realistic setting

Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography

One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive PNS-Machine Interfaces was convened, hosted by the International Conference on Rehabilitation Robotics. The keyword PNS-Machine Interface (PMI) has been selected to denote human-machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the peripheral nervous system (PNS) in a non-invasive way, that is, from the surface of the residuum. The workshop was intended to provide an overview of the state of the art and future perspectives of such interfaces; this paper represents is a collection of opinions expressed by each and every researcher/group involved in it

Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography

abstract: One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive peripheral nervous system (PNS)–Machine Interfaces (MI; PMI) was convened, hosted by the International Conference on Rehabilitation Robotics. The keyword PMI has been selected to denote human–machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the PNS in a non-invasive way, that is, from the surface of the residuum. The workshop was intended to provide an overview of the state of the art and future perspectives of such interfaces; this paper represents is a collection of opinions expressed by each and every researcher/group involved in it

Optogenetic Brain Interfaces

The brain is a large network of interconnected neurons where each cell functions as a nonlinear processing element. Unraveling the mysteries of information processing in the complex networks of the brain requires versatile neurostimulation and imaging techniques. Optogenetics is a new stimulation method which allows the activity of neurons to be modulated by light. For this purpose, the cell-types of interest are genetically targeted to produce light-sensitive proteins. Once these proteins are expressed, neural activity can be controlled by exposing the cells to light of appropriate wavelengths. Optogenetics provides a unique combination of features, including multimodal control over neural function and genetic targeting of specific cell-types. Together, these versatile features combine to a powerful experimental approach, suitable for the study of the circuitry of psychiatric and neurological disorders. The advent of optogenetics was followed by extensive research aimed to produce new lines of light-sensitive proteins and to develop new technologies: for example, to control the distribution of light inside the brain tissue or to combine optogenetics with other modalities including electrophysiology, electrocorticography, nonlinear microscopy, and functional magnetic resonance imaging. In this paper, the authors review some of the recent advances in the field of optogenetics and related technologies and provide their vision for the future of the field.United States. Defense Advanced Research Projects Agency (Space and Naval Warfare Systems Center, Pacific Grant/Contract No. N66001-12-C-4025)University of Wisconsin--Madison (Research growth initiative; grant 101X254)University of Wisconsin--Madison (Research growth initiative; grant 101X172)University of Wisconsin--Madison (Research growth initiative; grant 101X213)National Science Foundation (U.S.) (MRSEC DMR-0819762)National Science Foundation (U.S.) (NSF CAREER CBET-1253890)National Institutes of Health (U.S.) (NIH/NIBIB R00 Award (4R00EB008738)National Institutes of Health (U.S.) (NIH Director’s New Innovator award (1-DP2-OD002989))Okawa Foundation (Research Grant Award)National Institutes of Health (U.S.) (NIH Director’s New Innovator Award (1DP2OD007265))National Science Foundation (U.S.) (NSF CAREER Award (1056008)Alfred P. Sloan Foundation (Fellowship)Human Frontier Science Program (Strasbourg, France) (Grant No. 1351/12)Israeli Centers of Research Excellence (I-CORE grant, program 51/11)MINERVA Foundation (Germany

Co-Adaptive Control of Bionic Limbs via Unsupervised Adaptation of Muscle Synergies

Objective: In this work, we present a myoelectric interface that extracts natural motor synergies from multi-muscle signals and adapts in real-time with new user inputs. With this unsupervised adaptive myocontrol (UAM) system, optimal synergies for control are continuously co-adapted with changes in user motor control, or as a function of perturbed conditions via online non-negative matrix factorization guided by physiologically informed sparseness constraints in lieu of explicit data labelling. Methods: UAM was tested in a set of virtual target reaching tasks completed by able-bodied and amputee subjects. Tests were conducted under normative and electrode perturbed conditions to gauge control robustness with comparisons to non-adaptive and supervised adaptive myocontrol schemes. Furthermore, UAM was used to interface an amputee with a multi-functional powered hand prosthesis during standardized Clothespin Relocation Tests, also conducted in normative and perturbed conditions. Results: In virtual tests, UAM effectively mitigated performance degradation caused by electrode displacement, affording greater resilience over an existing supervised adaptive system for amputee subjects. Induced electrode shifts also had negligible effect on the real world control performance of UAM with consistent completion times (23.91 +/- 1.33 s) achieved across Clothespin Relocation Tests in the normative and electrode perturbed conditions. Conclusion: UAM affords comparable robustness improvements to existing supervised adaptive myocontrol interfaces whilst providing additional practical advantages for clinical deployment. Significance: The proposed system uniquely incorporates neuromuscular control principles with unsupervised online learning methods and presents a working example of a freely co-adaptive bionic interface.Peer reviewe

Principles of Neuromorphic Photonics

In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation industries in artificial intelligence services and high-performance computing are so far supported by microelectronic platforms. These data-intensive enterprises rely on continual improvements in hardware. Their prospects are running up against a stark reality: conventional one-size-fits-all solutions offered by digital electronics can no longer satisfy this need, as Moore's law (exponential hardware scaling), interconnection density, and the von Neumann architecture reach their limits. With its superior speed and reconfigurability, analog photonics can provide some relief to these problems; however, complex applications of analog photonics have remained largely unexplored due to the absence of a robust photonic integration industry. Recently, the landscape for commercially-manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of \emph{neuromorphic photonics}. This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.Comment: 28 pages, 19 figure