Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation.

After an initial period of recovery, human neurological injury has long been thought to be static. In order to improve quality of life for those suffering from stroke, spinal cord injury, or traumatic brain injury, researchers have been working to restore the nervous system and reduce neurological deficits through a number of mechanisms. For example, neurobiologists have been identifying and manipulating components of the intra- and extracellular milieu to alter the regenerative potential of neurons, neuro-engineers have been producing brain-machine and neural interfaces that circumvent lesions to restore functionality, and neurorehabilitation experts have been developing new ways to revitalize the nervous system even in chronic disease. While each of these areas holds promise, their individual paths to clinical relevance remain difficult. Nonetheless, these methods are now able to synergistically enhance recovery of native motor function to levels which were previously believed to be impossible. Furthermore, such recovery can even persist after training, and for the first time there is evidence of functional axonal regrowth and rewiring in the central nervous system of animal models. To attain this type of regeneration, rehabilitation paradigms that pair cortically-based intent with activation of affected circuits and positive neurofeedback appear to be required-a phenomenon which raises new and far reaching questions about the underlying relationship between conscious action and neural repair. For this reason, we argue that multi-modal therapy will be necessary to facilitate a truly robust recovery, and that the success of investigational microscopic techniques may depend on their integration into macroscopic frameworks that include task-based neurorehabilitation. We further identify critical components of future neural repair strategies and explore the most updated knowledge, progress, and challenges in the fields of cellular neuronal repair, neural interfacing, and neurorehabilitation, all with the goal of better understanding neurological injury and how to improve recovery

Is implicit motor imagery a reliable strategy for a brain computer interface?

Explicit motor imagery (eMI) is a widely used brain computer interface (BCI) paradigm, but not everybody can accomplish this task. Here we propose a BCI based on implicit motor imagery (iMI). We compared classification accuracy between eMI and iMI of hands. Fifteen able bodied people were asked to judge the laterality of hand images presented on a computer screen in a lateral or medial orientation. This judgement task is known to require mental rotation of a person’s own hands which in turn is thought to involve iMI. The subjects were also asked to perform eMI of the hands. Their electroencephalography (EEG) was recorded. Linear classifiers were designed based on common spatial patterns. For discrimination between left and right hand the classifier achieved maximum of 81 ± 8% accuracy for eMI and 83 ± 3% for iMI. These results show that iMI can be used to achieve similar classification accuracy as eMI. Additional classification was performed between iMI in medial and lateral orientations of a single hand; the classifier achieved 81 ± 7% for the left and 78 ± 7% for the right hand which indicate distinctive spatial patterns of cortical activity for iMI of a single hand in different directions. These results suggest that a special brain computer interface based on iMI may be constructed, for people who cannot perform explicit imagination, for rehabilitation of movement or for treatment of bodily spatial neglect

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

Enhancement of Robot-Assisted Rehabilitation Outcomes of Post-Stroke Patients Using Movement-Related Cortical Potential

Post-stroke rehabilitation is essential for stroke survivors to help them regain independence and to improve their quality of life. Among various rehabilitation strategies, robot-assisted rehabilitation is an efficient method that is utilized more and more in clinical practice for motor recovery of post-stroke patients. However, excessive assistance from robotic devices during rehabilitation sessions can make patients perform motor training passively with minimal outcome. Towards the development of an efficient rehabilitation strategy, it is necessary to ensure the active participation of subjects during training sessions. This thesis uses the Electroencephalography (EEG) signal to extract the Movement-Related Cortical Potential (MRCP) pattern to be used as an indicator of the active engagement of stroke patients during rehabilitation training sessions. The MRCP pattern is also utilized in designing an adaptive rehabilitation training strategy that maximizes patients’ engagement. This project focuses on the hand motor recovery of post-stroke patients using the AMADEO rehabilitation device (Tyromotion GmbH, Austria). AMADEO is specifically developed for patients with fingers and hand motor deficits. The variations in brain activity are analyzed by extracting the MRCP pattern from the acquired EEG data during training sessions. Whereas, physical improvement in hand motor abilities is determined by two methods. One is clinical tests namely Fugl-Meyer Assessment (FMA) and Motor Assessment Scale (MAS) which include FMA-wrist, FMA-hand, MAS-hand movements, and MAS-advanced hand movements’ tests. The other method is the measurement of hand-kinematic parameters using the AMADEO assessment tool which contains hand strength measurements during flexion (force-flexion), and extension (force-extension), and Hand Range of Movement (HROM)

Upravljanje robotom pomoću anticipacijskih potencijala mozga

Recently Biomedical Engineering showed advances in using brain potentials for control of physical devices, in particular, robots. This paper is focused on controlling robots using anticipatory brain potentials. An oscillatory brain potential generated in the CNV Flip-Flop Paradigm is used to trigger sequence of robot behaviors. Experimental illustration is given in which two robotic arms, driven by a brain expectancy potential oscillation, cooperatively solve the well known problem of Towers of Hanoi.U posljednje vrijeme je u području biomedicinskog inženjerstva postignut napredak u korištenju potencijala mozga za upravljanje fizičkim napravama, posebice robotima. U radu je opisana mogućnost upravljanja robotima pomoću anticipacijskih potencijala mozga. Oscilacijski potencijal mozga generiran u CNV (Contingent Negative Variation) flip-flop paradigmi se koristi za okidanje slijeda ponašanja robota. U radu je prikazana eksperimentalna ilustracija rješavanja dobro poznatog problema Hanojskih tornjeva pomoću dvije robotske ruke upravljane moždanim potencijalom očekivanja

EEG-Based Brain-Computer Interfacing via Motor-Imagery: Practical Implementation and Feature Analysis

The human brain is the most intriguing and complex signal processing unit ever known to us. A unique characteristic of our brain is its plasticity property, i.e., the ability of neurons to modify their behavior (structure and functionality) in response to environmental diversity. The plasticity property of brain has motivated design of brain-computer interfaces (BCI) to develop an alternative form of communication channel between brain signals and the external world. The BCI systems have several therapeutic applications of significant importance including but not limited to rehabilitation/ assistive systems, rehabilitation robotics, and neuro-prosthesis control. Despite recent advancements in BCIs, such systems are still far from being reliably incorporated within humanmachine inference networks. In this regard, the thesis focuses on Motor Imagery (MI)-based BCI systems with the objective of tackling some key challenges observed in existing solutions. The MI is defined as a cognitive process in which a person imagines performing a movement without peripheral (muscle) activation. At one hand, the thesis focuses on feature extraction, which is one of the most crucial steps for the development of an effective BCI system. In this regard, the thesis proposes a subject-specific filtering framework, referred to as the regularized double-band Bayesian (R-B2B) spectral filtering. The proposed R-B2B framework couples three main feature extraction categories, namely filter-bank solutions, regularized techniques, and optimized Bayesian mechanisms to enhance the overall classification accuracy of the BCI. To further evaluate the effects of deploying optimized subject-specific spectra-spatial filters, it is vital to examine and investigate different aspects of data collection and in particular, effects of the stimuli provided to subjects to trigger MI tasks. The second main initiative of the thesis is to propose an element of experimental design dealing with MI-based BCI systems. In this regard, we have implemented an EEG-based BCI system and constructed a benchmark dataset associated with 10 healthy subjects performing actual movement and MI tasks. To investigate effects of stimulus on the overall achievable performance, four different protocols are designed and implemented via introduction of visual and voice stimuli. Finally, the work investigates effects of adaptive trimming of EEG epochs resulting in an adaptive and subject-specific solution