BCI-assisted training for upper limb motor rehabilitation: estimation of effects on individual brain connectivity and motor functions

The aim of the study is to quantify individual changes in scalp connectivity patterns associated to the affected hand movement in stroke patients after a 1-month training based on BCIsupported motor imagery to improve upper limb motor recovery. To perform the statistical evaluation between pre- and post-training conditions at the single subject level, a resampling approach was applied to EEG datasets acquired from 12 stroke patients during the execution of a motor task with the stroke affected hand before and after the rehabilitative intervention. Significant patterns of the network reinforced after the training were extracted and a significant correlation was found between indices related to the reinforced pattern and the clinical outcome indicated by clinical scales

Optimization methodologies study for the development of prognostic artificial neural network

In this work, we discuss the implementation and optimization of an artificial neural network (ANN) based on the analysis of the back-EMF coefficient capable of making electromechanical actuator (EMA) prognostics. Starting from the pseudorandom generation of failure values related to static rotor eccentricity and partial short circuit of the stator coils, we simulated through a MATLAB-Simulink model the values of currents, voltages, position and angular velocity of the rotor and thanks to these we obtained the back-electromotive force which represents the input layer of the ANN. In this paper, we will turn our attention to optimizing the hyperparameters which influence supervised learning and make it more performing in terms of computational cost and complexity. The results are satisfactory dealing with the number of examples present in the available dataset

Biomimetic microelectronics for regenerative neuronal cuff implants

Smart biomimetics, a unique class of devices combining the mechanical adaptivity of soft actuators with the imperceptibility of microelectronics, is introduced. Due to their inherent ability to self‐assemble, biomimetic microelectronics can firmly yet gently attach to an inorganic or biological tissue enabling enclosure of, for example, nervous fibers, or guide the growth of neuronal cells during regeneration

Phase Nanoengineering via Thermal Scanning Probe Lithography and Direct Laser Writing

Nanomaterials derive their electronic, magnetic, and optical properties from their specific nanostructure. In most cases, nanostructured materials and their properties are defined during the materials growth, and nanofabrication techniques, such as lithography, are employed subsequently for device fabrication. Herein, a perspective is presented on a different approach for creating nanomaterials and devices where, after growth, advanced nanofabrication techniques are used to directly nanostructure condensed matter systems, by inducing highly controlled, localized, and stable changes in the electronic, magnetic, or optical properties. Then, advantages, limitations, applications in materials science and technology are highlighted, and future perspectives are discussed

Thermochemical scanning probe lithography of protein gradients at the nanoscale

Patterning nanoscale protein gradients is crucial for studying a variety of cellular processes in vitro. Despite the recent development in nano-fabrication technology, combining nanometric resolution and fine control of protein concentrations is still an open challenge. Here, we demonstrate the use of thermochemical scanning probe lithography (tc-SPL) for defining micro- and nano-sized patterns with precisely controlled protein concentration. First, tc-SPL is performed by scanning a heatable atomic force microscopy tip on a polymeric substrate, for locally exposing reactive amino groups on the surface, then the substrate is functionalized with streptavidin and laminin proteins. We show, by fluorescence microscopy on the patterned gradients, that it is possible to precisely tune the concentration of the immobilized proteins by varying the patterning parameters during tc-SPL. This paves the way to the use of tc-SPL for defining protein gradients at the nanoscale, to be used as chemical cues e.g. for studying and regulating cellular processes in vitro