Featured BMI Workshop

Problems and Solutions in Developing Noninvasive BMI to Powered Power-limb Exoskeleton Systems for Restoration and Rehabilitation of Gait

Jose L. Contreras-Vidal, Ph.D. University of Maryland, USA

Breakthroughs in the non-invasive decoding of real/visual/imagined movement and movement intent from scalp electroencephalography (EEG) now enable accurate prediction of upper extremity movement and gait in humans to reliably drive a BMI. The EEG time-domain decoders enable subjects to accomplish closed-loop brain to computer interface control in a single session, making noninvasive BMI systems to control multi-functional prosthetics/orthotics clinically feasible. In this talk I will outline the engineering, clinical, and time-permitting, regulatory problems and solutions that we need to overcome to develop a reliable EEG-based BMI system to powered lower-limb exoskeletons for clinical use in patients with spinal cord injury and stroke.

Implantable Brain-Machine Wireless Interfaces for Biosensing and Treatment of Neural Dysfunctions

Mohamad Sawan, Ph.D. Polytechnique University of Montreal, Canada

Brain-Machine Interfaces, dedicated for biosensing and subsequent treatment of cortical neural dysfunctions, are promising alternative for monitoring and studying brain activities underlying cognitive functions and pathologies. This talk covers circuits and systems techniques used for the implementation and integration of biomedical devices. In particular, primary visual cortex stimulation and epileptic seizures detection will be described. Also treatment through microelectrostimulation and drug delivery techniques will be summarized. Global view of typical devices altogether with corresponding multidimensional design and implementation challenges such power management and high-data rate communication modules will be explained.

A Miniaturized System for Spike-Triggered Intracortical Microstimulation in a Brain-Machine-Brain Interface

Pedram Mohseni, Ph.D. Case Western Reserve University, Cleveland, USA

To date, brain-machine interfaces (BMIs) have sought to interface the brain with the external world using intrinsic neuronal signals as input commands for controlling external devices, or device-generated electrical signals to mimic sensory inputs to the nervous system. A new generation of neuroprostheses is now emerging that aims to combine neural recording, signal processing, and microstimulation functionalities for closed-loop operation. These devices might use information extracted from the brain neural activity to trigger microstimulation or modulate stimulus parameters in real time, potentially enhancing the clinical efficacy of neuromodulation in alleviating pathologic symptoms or restoring lost sensory and motor functions in the disabled. This talk will report on a miniaturized system for spike-triggered intracortical microstimulation as a novel, device-based approach for improving functional recovery after traumatic brain injury (TBI). The neurophysiological rationale behind this work and our current findings from experiments with anesthetized and ambulatory, brain-injured rats using a battery-powered, head-mounted microdevice will be presented. This work has the potential to remarkably advance the neurorehabilitation field at the level of functional neurons and networks.

Neural Adaptations to a Brain-Machine Interface

Jose Carmena, Ph.D. University of California, Berkeley, USA

The advent of multi-electrode recordings and brain-machine interfaces (BMIs) has provided a powerful tool for the development of neuroprosthetic systems for people with sensory and motor disabilities. BMIs are powerful tools that use brain-derived signals to control artificial devices such as computer cursors and robots. By recording the electrical activity of hundreds of neurons from multiple cortical areas in subjects performing motor tasks we can study the spatio-temporal patterns of neural activity and quantify the neurophysiological changes occurring in cortical networks, both in manual and brain control modes of operation. In this talk I will present exciting results from our lab showing that the brain can consolidate prosthetic motor skill in a way that resembles that of natural motor learning. Using stable recording from ensembles of units from primary motor cortex in two macaque monkeys we demonstrate that proficient neuroprosthetic control reversibly reshapes cortical networks through local effects. This will be followed by an outline on the emerging directions the field is taking towards the development of neuroprosthetic devices for the impaired.

ÒIntelligentÓ Neuroprosthetics: System Design Strategies from Lab Bench to Patient Bedside

Justin C. Sanchez, Ph.D. University of Miami, USA

Closed-loop neural interfaces have the great potential for treating neurological disorders and restoring communication/control in disabled individuals. The transformative aspect of closed-loop neural interfaces is that they can be designed as Òintelligent toolsÓ that have the capability to assist, evolve, and grow with the user. Unlike many other devices, neural interfaces exist in a shared space that seamlessly spans the user's internal, adaptive representation of the world and the physical environment enabling a much deeper human-device symbiosis. Recent advancements in the neuroscience and engineering of neural interfaces are providing a blueprint for neurophysiologic hardware design and methods for determining representation in multiscale signals for deriving therapeutic strategies. This talk covers recent advances in science and technology supporting the development of intelligent neural interfaces from the bench to bedside and contrasts them with Òlessons learnedÓ from the past 20 years of neural interface design.

Bypassing the Injured Spinal Cord: Toward Practical, Cortically Controlled Functional Electrical Stimulation

Lee E. Miller, Ph.D., Christian Ethier, Emily R. Oby, & Nicholas Sachs Northwestern University, Chicago, USA

Spinal cord injury causes a devastating loss of independence as well as a host of adverse physiological changes including loss of muscle tone and bone density, and compromised cardiovascular system. Functional Electrical Stimulation (FES) can be used to produce contraction of paralyzed muscles, and such efforts are underway to develop systems that would provide reach and grasp, bowel and bladder control, stance, and locomotion. Indirectly, FES can also significantly improve the overall health of spinal injured patients through the exercise it generates. Current approaches to the restoration of grasp function for patients with mid-cervical injuries use pre-programmed patterns of stimulation that are controlled by the patient using simple signals derived from residual movement of the proximal limb. However, this limits available hand functions as well as the range of patients for which it is suitable. In contrast, we have developed an FES prosthesis that is controlled by signals recorded from a multi-electrode array implanted in the motor cortex. The system has allowed several monkeys to regain voluntary control of simple grasping movements despite temporary paralysis induced by peripheral nerve block. We are currently working to implement a number of improvements to the system: 1) We need to increase the number of independently controlled degrees of freedom through nerve, as well as intramuscular stimulation. 2) The controller must be readily recalibrated to accommodate lost or altered neural signals, but these changes must not interfere with parallel adaptive changes made by the user. 3) The system must be able to adapt to and generalize across a range of different dynamical and postural settings without recalibration and without access to the EMG signals from the normal monkey. If we are able to solve these problems using a monkey model in the laboratory setting, we believe that a similar brain-controlled FES prostheses might ultimately benefit even patients with high cervical injuries who lack the ability to control proximal arm movements.

Cortical Signals and Decoding Strategies for Dexterous Prosthesis

Nitish V. Thakor, Ph.D. Johns Hopkins University, Baltimore, USA

A prosthetics revolution is underway - with quantum advancements in the design of dexterous prosthesis developed only within the past few years. With the availability of limbs with anthropomorphic designs that possess as many as 22 degrees of freedom, the challenge now is to develop control strategies for these limbs. The choices are myoelectric and neural interfaces. This presentation will review signals and decoding strategies based on myoelectric and neural signals (Electrocorticograms, local field potentials, and spikes) for control of dexterous limb movement, including individuated fingers and grasps. While myoelectric control is noninvasive, its decoding capability is limited, it is prone to electrode attachment problems, and is not intuitive. Neural control offers long term promise but the challenge is to decipher the cortical signals from population of neurons in different cortical regions. Our work shows that Electrocorticographic signals can provide low dimensional decoding of finger movements, while spikes and local field potentials provide finer and more accurate decoding of dexterous movements. This talk will present the generalized linear models and how population codes from neurons can be used to achieve high decoding accuracies. Finally, the challenge of utilizing the vast cortical resources for achieving high dimensional neural control of state of the art dexterous prostheses will be presented.