Flexible Neural Electrode Array Based-on Porous Graphene for Cortical Microstimulation and Sensing.

Neural sensing and stimulation have been the backbone of neuroscience research, brain-machine interfaces and clinical neuromodulation therapies for decades. To-date, most of the neural stimulation systems have relied on sharp metal microelectrodes with poor electrochemical properties that induce extensive damage to the tissue and significantly degrade the long-term stability of implantable systems. Here, we demonstrate a flexible cortical microelectrode array based on porous graphene, which is capable of efficient electrophysiological sensing and stimulation from the brain surface, without penetrating into the tissue. Porous graphene electrodes show superior impedance and charge injection characteristics making them ideal for high efficiency cortical sensing and stimulation. They exhibit no physical delamination or degradation even after 1 million biphasic stimulation cycles, confirming high endurance. In in vivo experiments with rodents, same array is used to sense brain activity patterns with high spatio-temporal resolution and to control leg muscles with high-precision electrical stimulation from the cortical surface. Flexible porous graphene array offers a minimally invasive but high efficiency neuromodulation scheme with potential applications in cortical mapping, brain-computer interfaces, treatment of neurological disorders, where high resolution and simultaneous recording and stimulation of neural activity are crucial

A Miniature Robot for Isolating and Tracking Neurons in Extracellular Cortical Recordings

This paper presents a miniature robot device and control algorithm that can autonomously position electrodes in cortical tissue for isolation and tracking of extracellular signals of individual neurons. Autonomous electrode positioning can significantly enhance the efficiency and quality of acute electrophysiolgical experiments aimed at basic understanding of the nervous system. Future miniaturized systems of this sort could also overcome some of the inherent difficulties in estabilishing long-lasting neural interfaces that are needed for practical realization of neural prostheses. The paper describes the robot's design and summarizes the overall structure of the control system that governs the electrode positioning process. We present a new sequential clustering algorithm that is key to improving our system's performance, and which may have other applications in robotics. Experimental results in macaque cortex demonstrate the validity of our approach

Implantable devices for optical neural interfaces

pre-printOptical neural control requires light delivery techniques dependent on the experimental goal and biological model. Several light sources and neural interfaces have been implemented featuring one or more of the fol- lowing criteria: deep illumination, specific and/or com- prehensive access, spectral control, temporal precision, high resolution patterning. We've developed 3D needle- type waveguide arrays as potentially compact neural interfaces for light transmission of as much as 90% of input light to depths >1mm in tissue; various experimental paradigms are easily accommodated as the arrays can be modified to project different illumination volumes at defined depths, wavelengths and patterns

New Perspectives on the Dialogue between Brains and Machines

Brain-machine interfaces (BMIs) are mostly investigated as a means to provide paralyzed people with new communication channels with the external world. However, the communication between brain and artificial devices also offers a unique opportunity to study the dynamical properties of neural systems. This review focuses on bidirectional interfaces, which operate in two ways by translating neural signals into input commands for the device and the output of the device into neural stimuli. We discuss how bidirectional BMIs help investigating neural information processing and how neural dynamics may participate in the control of external devices. In this respect, a bidirectional BMI can be regarded as a fancy combination of neural recording and stimulation apparatus, connected via an artificial body. The artificial body can be designed in virtually infinite ways in order to observe different aspects of neural dynamics and to approximate desired control policies

Autonomy Infused Teleoperation with Application to BCI Manipulation

Robot teleoperation systems face a common set of challenges including latency, low-dimensional user commands, and asymmetric control inputs. User control with Brain-Computer Interfaces (BCIs) exacerbates these problems through especially noisy and erratic low-dimensional motion commands due to the difficulty in decoding neural activity. We introduce a general framework to address these challenges through a combination of computer vision, user intent inference, and arbitration between the human input and autonomous control schemes. Adjustable levels of assistance allow the system to balance the operator's capabilities and feelings of comfort and control while compensating for a task's difficulty. We present experimental results demonstrating significant performance improvement using the shared-control assistance framework on adapted rehabilitation benchmarks with two subjects implanted with intracortical brain-computer interfaces controlling a seven degree-of-freedom robotic manipulator as a prosthetic. Our results further indicate that shared assistance mitigates perceived user difficulty and even enables successful performance on previously infeasible tasks. We showcase the extensibility of our architecture with applications to quality-of-life tasks such as opening a door, pouring liquids from containers, and manipulation with novel objects in densely cluttered environments

Improvement of the Material's Mechanical Characteristics using Intelligent Real Time Control Interfaces in HFC Hardening Process

The paper presents Intelligent Control (IC) Interfaces for real time control of mechatronic systems applied to Hardening Process Control (HPC) in order to improvement of the material's mechanical characteristics. Implementation of IC laws in the intelligent real time control interfaces depends on the particular circumstances of the models characteristics used and the exact definition of optimization problem. The results led to the development of the IC interfaces in real time through Particle Swarm Optimization (PSO) and neural networks (NN) using offline the regression methods

Improvement of the Material\u27s Mechanical Characteristics using Intelligent Real Time Control Interfaces in HFC Hardening Process

The paper presents Intelligent Control (IC) Interfaces for real time control of mechatronic systems applied to Hardening Process Control (HPC) in order to improvement of the material\u27s mechanical characteristics. Implementation of IC laws in the intelligent real time control interfaces depends on the particular circumstances of the models characteristics used and the exact definition of optimization problem. The results led to the development of the IC interfaces in real time through Particle Swarm Optimization (PSO) and neural networks (NN) using offline the regression methods

Cognitive based neural prosthetics

Intense activity in neural prosthetic research has recently demonstrated the possibility of robotic interfaces that respond directly to the nervous system. The question remains of how the flow of information between the patient and the prosthetic device should be designed to provide a safe, effective system that maximizes the patient’s access to the outside world. Much recent work by other investigators has focused on using decoded neural signals as low-level commands to directly control the trajectory of screen cursors or robotic end-effectors. Here we review results that show that high-level, or cognitive, signals can be decoded from planned arm movements. These results, coupled with fundamental limitations in signal recording technology, motivate an approach in which cognitive neural signals play a larger role in the neural interface. This proposed paradigm predicates that neural signals should be used to instruct external devices, rather than control their detailed movement. This approach will reduce the effort required of the patient and will take advantage of established and on-going robotics research in intelligent systems and human-robot interfaces