Neuroplasticity subserving the operation of brain-machine interfaces

Advances in brain-computer interface controlled functional electrical stimulation for upper limb recovery after stroke

Stroke often results in varying degrees of functional impairment, significantly affecting patients' quality of daily life. In recent years, brain-computer interface-controlled functional electrical stimulation has offered new therapeutic approaches for post-stroke rehabilitation. This paper reviews the application of BCI-FES in the recovery of upper limb function after stroke and explores its underlying mechanisms. By analyzing relevant studies, the aim is to provide a theoretical basis for rehabilitating upper limb function post-stroke, promote BCI-FES, and offer guidance for future clinical practice.

Decoding Single-Pellet Retrieval Task From Local Field Potentials in Pre- and Post-Stroke Motor Areas: Insights Into Interhemispheric Connectivity Difference

OBJECTIVE

Intracortical brain-machine interfaces (iBMIs) hold promise for restoring communication and movement in stroke-paralyzed individuals. Recent studies have demonstrated the potential of using local field potentials (LFPs) for decoding single-pellet retrieval (SPR) tasks in iBMIs. However, most research has relied on LFPs from healthy rats rather than those affected by stroke. This study aimed to investigate the feasibility of utilizing LFPs from both the right and left (stroke) cortical forelimb areas (CFAs) for the SPR tasks decoding under both pre- and post-stroke conditions.

METHODS

LFPs were recorded via microelectrode arrays implanted into CFAs of eight rats trained to perform the SPR tasks. The relative spectral power method was used to represent frequency information, and random forest classification differentiated SPR tasks from resting states. We also assessed interhemispheric connectivity, including correlation, coherence, and phase-amplitude coupling (PAC), to compare differences between the SPR tasks and the resting states under both pre- and post-stroke conditions.

RESULTS

Our findings indicated that the relative PS method with LFPs achieves 87.10% 9.2% accuracy in post-stoke SPR decoding, where high gamma is crucial. Additionally, we observed changes in PACs from the right to the left sensorimotor cortex post-stroke during the SPR tasks compared to the resting states.

SIGNIFICANCE

Our work provides a comprehensive insight into the role of different frequency band from LFPs in motor function recovery mechanisms, highlighting the importance of the high gamma in motor function. This research lays the foundation for developing post-stoke SPR-related BMIs.

Application of the participatory design in the testing of a baropodometric insole prototype for weight-bearing asymmetry after a stroke: A qualitative study

Introduction

Currently studies indicate the need to incorporate the user`s perspective in the testing of new assistive technologies. The objective of this paper is to test a baropodometric insole prototype for monitoring and treatment weight-bearing asymmetry, according to the Participatory Design.

Methods

We used a qualitative case study approach during the testing phase of the baropodometric insole prototype. The focus group approach addressed topics related to the experience and accessibility of the potential user in conjunction with professionals, researchers, and physiotherapy students. Facilitators, barriers, and requirements for the device were collected through audio recordings of the discussions during and after prototype testing.

Results

Key steps in the prototype testing process were divided into (1) Test of the prototype according to the Participatory Design, divided into Who, When, How, and Why the potential user was involved in the study; and (2) Facilitators, barriers and requirements to improve the prototype.

Conclusions

The baropodometric insole prototype can be seen as a promising device for monitoring and treating weight-bearing asymmetry.

Compressive sensing of functional connectivity maps from patterned optogenetic stimulation of neuronal ensembles

Mapping functional connectivity between neurons is an essential step toward probing the neural computations mediating behavior. Accurately determining synaptic connectivity maps in populations of neurons is challenging in terms of yield, accuracy, and experimental time. Here, we developed a compressive sensing approach to reconstruct synaptic connectivity maps based on random two-photon cell-targeted optogenetic stimulation and membrane voltage readout of many putative postsynaptic neurons. Using a biophysical network model of interconnected populations of excitatory and inhibitory neurons, we characterized mapping recall and precision as a function of network observability, sparsity, number of neurons stimulated, off-target stimulation, synaptic reliability, propagation latency, and network topology. We found that mapping can be achieved with far fewer measurements than the standard pairwise sequential approach, with network sparsity and synaptic reliability serving as primary determinants of the performance. Our results suggest a rapid and efficient method to reconstruct functional connectivity of sparsely connected neuronal networks.

Prediction of Gait Kinematics and Kinetics: A Systematic Review of EMG and EEG Signal Use and Their Contribution to Prediction Accuracy

Human-machine interfaces hold promise in enhancing rehabilitation by predicting and responding to subjects' movement intent. In gait rehabilitation, neural network architectures utilize lower-limb muscle and brain activity to predict continuous kinematics and kinetics during stepping and walking. This systematic review, spanning five databases, assessed 16 papers meeting inclusion criteria. Studies predicted lower-limb kinematics and kinetics using electroencephalograms (EEGs), electromyograms (EMGs), or a combination with kinematic data and anthropological parameters. Long short-term memory (LSTM) and convolutional neural network (CNN) tools demonstrated highest accuracies. EEG focused on joint angles, while EMG predicted moments and torque joints. Useful EEG electrode locations included C3, C4, Cz, P3, F4, and F8. Vastus Lateralis, Rectus Femoris, and Gastrocnemius were the most commonly accessed muscles for kinematic and kinetic prediction using EMGs. No studies combining EEGs and EMGs to predict lower-limb kinematics and kinetics during stepping or walking were found, suggesting a potential avenue for future development in this technology.

Building an adaptive interface via unsupervised tracking of latent manifolds

In human-machine interfaces, decoder calibration is critical to enable an effective and seamless interaction with the machine. However, recalibration is often necessary as the decoder off-line predictive power does not generally imply ease-of-use, due to closed loop dynamics and user adaptation that cannot be accounted for during the calibration procedure. Here, we propose an adaptive interface that makes use of a non-linear autoencoder trained iteratively to perform online manifold identification and tracking, with the dual goal of reducing the need for interface recalibration and enhancing human-machine joint performance. Importantly, the proposed approach avoids interrupting the operation of the device and it neither relies on information about the state of the task, nor on the existence of a stable neural or movement manifold, allowing it to be applied in the earliest stages of interface operation, when the formation of new neural strategies is still on-going. In order to more directly test the performance of our algorithm, we defined the autoencoder latent space as the control space of a body-machine interface. After an initial offline parameter tuning, we evaluated the performance of the adaptive interface versus that of a static decoder in approximating the evolving low-dimensional manifold of users simultaneously learning to perform reaching movements within the latent space. Results show that the adaptive approach increased the representational efficiency of the interface decoder. Concurrently, it significantly improved users' task-related performance, indicating that the development of a more accurate internal model is encouraged by the online co-adaptation process.

Adenosine A2A receptor blockade improves neuroprosthetic learning by volitional control of population calcium signal in M1 cortical neurons

Volitional control is at the core of brain-machine interfaces (BMI) adaptation and neuroprosthetic-driven learning to restore motor function for disabled patients, but neuroplasticity changes and neuromodulation underlying volitional control of neuroprosthetic learning are largely unexplored. To better study volitional control at annotated neural population, we have developed an operant neuroprosthetic task with closed-loop feedback system by volitional conditioning of population calcium signal in the M1 cortex using fiber photometry recording. Importantly, volitional conditioning of the population calcium signal in M1 neurons did not improve within-session adaptation, but specifically enhanced across-session neuroprosthetic skill learning with reduced time-to-target and the time to complete 50 successful trials. With brain-behavior causality of the neuroprosthetic paradigm, we revealed that proficiency of neuroprosthetic learning by volitional conditioning of calcium signal was associated with the stable representational (plasticity) mapping in M1 neurons with the reduced calcium peak. Furthermore, pharmacological blockade of adenosine A_2A receptors facilitated volitional conditioning of neuroprosthetic learning and converted an ineffective volitional conditioning protocol to be the effective for neuroprosthetic learning. These findings may help to harness neuroplasticity for better volitional control of neuroprosthetic training and suggest a novel pharmacological strategy to improve neuroprosthetic learning in BMI adaptation by targeting striatal A_2A receptors.

Closed-Loop Neuromodulation in Physiological and Translational Research

Neuromodulation, the focused delivery of energy to neural tissue to affect neural or physiological processes, is a common method to study the physiology of the nervous system. It is also successfully used as treatment for disorders in which the nervous system is affected or implicated. Typically, neurostimulation is delivered in open-loop mode (i.e., according to a predetermined schedule and independently of the state of the organ or physiological system whose function is sought to be modulated). However, the physiology of the nervous system or the modulated organ can be dynamic, and the same stimulus may have different effects depending on the underlying state. As a result, open-loop stimulation may fail to restore the desired function or cause side effects. In such cases, a neuromodulation intervention may be preferable to be administered in closed-loop mode. In a closed-loop neuromodulation (CLN) system, stimulation is delivered when certain physiological states or conditions are met (responsive neurostimulation); the stimulation parameters can also be adjusted dynamically to optimize the effect of stimulation in real time (adaptive neurostimulation). In this review, the reasons and the conditions for using CLN are discussed, the basic components of a CLN system are described, and examples of CLN systems used in physiological and translational research are presented.

The use of EEG biofeedback to improve memory, concentration, attention and reduce emotional tension

Biofeedback is a method of giving patients computerised feedback signals about changes in the physiological state of their body. This allows them to learn how to consciously modify functions not controlled consciously. This method allows active and conscious involvement of the patient in controlling their own physiological processes. The therapy aims to regulate the frequency of human brain waves. The human brain produces different ranges of waves that are characteristic of different types of human activity, a mechanism used in this method. The use of this method in routine rehabilitation with a specifically designed computer programme provides physicians, physiotherapists, neuropsychologists and speech therapists with a new tool for treatment, opportunities for improvement in treatment, and helps them better plan and develop treatment strategies using Evidence-Based Medicine. The aim of the work is to discuss how EEG Biofeedback software can be applied in neurorehabilitation and to discuss the use of EEG Biofeedback software in order to improve memory, concentration, attention, reduce emotional tension, increase resistance to stress, improve self-control, self-esteem and relaxation. Key words EEG biofeedback, neurorehabilitation, computer software in rehabilitation Article received: 14.01.2019; Accepted: 17.09.2019

Classification of different reaching movements from the same limb using EEG

OBJECTIVE

Brain-computer-interfaces (BCIs) have been proposed not only as assistive technologies but also as rehabilitation tools for lost functions. However, due to the stochastic nature, poor spatial resolution and signal to noise ratio from electroencephalography (EEG), multidimensional decoding has been the main obstacle to implement non-invasive BCIs in real-live rehabilitation scenarios. This study explores the classification of several functional reaching movements from the same limb using EEG oscillations in order to create a more versatile BCI for rehabilitation.

APPROACH

Nine healthy participants performed four 3D center-out reaching tasks in four different sessions while wearing a passive robotic exoskeleton at their right upper limb. Kinematics data were acquired from the robotic exoskeleton. Multiclass extensions of Filter Bank Common Spatial Patterns (FBCSP) and a linear discriminant analysis (LDA) classifier were used to classify the EEG activity into four forward reaching movements (from a starting position towards four target positions), a backward movement (from any of the targets to the starting position and rest). Recalibrating the classifier using data from previous or the same session was also investigated and compared.

MAIN RESULTS

Average EEG decoding accuracy were significantly above chance with 67%, 62.75%, and 50.3% when decoding three, four and six tasks from the same limb, respectively. Furthermore, classification accuracy could be increased when using data from the beginning of each session as training data to recalibrate the classifier.

SIGNIFICANCE

Our results demonstrate that classification from several functional movements performed by the same limb is possible with acceptable accuracy using EEG oscillations, especially if data from the same session are used to recalibrate the classifier. Therefore, an ecologically valid decoding could be used to control assistive or rehabilitation mutli-degrees of freedom (DoF) robotic devices using EEG data. These results have important implications towards assistive and rehabilitative neuroprostheses control in paralyzed patients.

Progress in Neuroengineering for brain repair: New challenges and open issues

Background

In recent years, biomedical devices have proven to be able to target also different neurological disorders. Given the rapid ageing of the population and the increase of invalidating diseases affecting the central nervous system, there is a growing demand for biomedical devices of immediate clinical use. However, to reach useful therapeutic results, these tools need a multidisciplinary approach and a continuous dialogue between neuroscience and engineering, a field that is named neuroengineering. This is because it is fundamental to understand how to read and perturb the neural code in order to produce a significant clinical outcome.

Results

In this review, we first highlight the importance of developing novel neurotechnological devices for brain repair and the major challenges expected in the next years. We describe the different types of brain repair strategies being developed in basic and clinical research and provide a brief overview of recent advances in artificial intelligence that have the potential to improve the devices themselves. We conclude by providing our perspective on their implementation to humans and the ethical issues that can arise.

Conclusions

Neuroengineering approaches promise to be at the core of future developments for clinical applications in brain repair, where the boundary between biology and artificial intelligence will become increasingly less pronounced.

Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis

Brain‐computer interfaces (BCIs) can provide sensory feedback of ongoing brain oscillations, enabling stroke survivors to modulate their sensorimotor rhythms purposefully. A number of recent clinical studies indicate that repeated use of such BCIs might trigger neurological recovery and hence improvement in motor function. Here, we provide a first meta‐analysis evaluating the clinical effectiveness of BCI‐based post‐stroke motor rehabilitation. Trials were identified using MEDLINE, CENTRAL, PEDro and by inspection of references in several review articles. We selected randomized controlled trials that used BCIs for post‐stroke motor rehabilitation and provided motor impairment scores before and after the intervention. A random‐effects inverse variance method was used to calculate the summary effect size. We initially identified 524 articles and, after removing duplicates, we screened titles and abstracts of 473 articles. We found 26 articles corresponding to BCI clinical trials, of these, there were nine studies that involved a total of 235 post‐stroke survivors that fulfilled the inclusion criterion (randomized controlled trials that examined motor performance as an outcome measure) for the meta‐analysis. Motor improvements, mostly quantified by the upper limb Fugl‐Meyer Assessment (FMA‐UE), exceeded the minimal clinically important difference (MCID=5.25) in six BCI studies, while such improvement was reached only in three control groups. Overall, the BCI training was associated with a standardized mean difference of 0.79 (95% CI: 0.37 to 1.20) in FMA‐UE compared to control conditions, which is in the range of medium to large summary effect size. In addition, several studies indicated BCI‐induced functional and structural neuroplasticity at a subclinical level. This suggests that BCI technology could be an effective intervention for post‐stroke upper limb rehabilitation. However, more studies with larger sample size are required to increase the reliability of these results.

Changes in cortical network connectivity with long-term brain-machine interface exposure after chronic amputation

Studies on neural plasticity associated with brain-machine interface (BMI) exposure have primarily documented changes in single neuron activity, and largely in intact subjects. Here, we demonstrate significant changes in ensemble-level functional connectivity among primary motor cortical (MI) neurons of chronically amputated monkeys exposed to control a multiple-degree-of-freedom robot arm. A multi-electrode array was implanted in M1 contralateral or ipsilateral to the amputation in three animals. Two clusters of stably recorded neurons were arbitrarily assigned to control reach and grasp movements, respectively. With exposure, network density increased in a nearly monotonic fashion in the contralateral monkeys, whereas the ipsilateral monkey pruned the existing network before re-forming a denser connectivity. Excitatory connections among neurons within a cluster were denser, whereas inhibitory connections were denser among neurons across the two clusters. These results indicate that cortical network connectivity can be modified with BMI learning, even among neurons that have been chronically de-efferented and de-afferented due to amputation.

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation

Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links between living brains and artificial actuators. Although theoretical propositions and some proof of concept experiments on directly linking the brains with machines date back to the early 1960s, BMI research only took off in earnest at the end of the 1990s, when this approach became intimately linked to new neurophysiological methods for sampling large-scale brain activity. The classic goals of BMIs are 1) to unveil and utilize principles of operation and plastic properties of the distributed and dynamic circuits of the brain and 2) to create new therapies to restore mobility and sensations to severely disabled patients. Over the past decade, a wide range of BMI applications have emerged, which considerably expanded these original goals. BMI studies have shown neural control over the movements of robotic and virtual actuators that enact both upper and lower limb functions. Furthermore, BMIs have also incorporated ways to deliver sensory feedback, generated from external actuators, back to the brain. BMI research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain's body schema. Work on BMIs has also led to the introduction of novel neurorehabilitation strategies. As a result of these efforts, long-term continuous BMI use has been recently implicated with the induction of partial neurological recovery in spinal cord injury patients.

Intracortical Brain-Machine Interfaces Advance Sensorimotor Neuroscience

Brain-machine interfaces (BMIs) decode brain activity to control external devices. Over the past two decades, the BMI community has grown tremendously and reached some impressive milestones, including the first human clinical trials using chronically implanted intracortical electrodes. It has also contributed experimental paradigms and important findings to basic neuroscience. In this review, we discuss neuroscience achievements stemming from BMI research, specifically that based upon upper limb prosthetic control with intracortical microelectrodes. We will focus on three main areas: first, we discuss progress in neural coding of reaches in motor cortex, describing recent results linking high dimensional representations of cortical activity to muscle activation. Next, we describe recent findings on learning and plasticity in motor cortex on various time scales. Finally, we discuss how bidirectional BMIs have led to better understanding of somatosensation in and related to motor cortex.