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Poly-articulated, myoelectric hand prostheses reproduce complex multi-degree of freedom movements, which are fundamental to effectively assist upper limb amputees in the execution of daily life activities. In this scenario, the control system consists in a pattern recognition algorithm translating the recorded electromyographic (EMG) activity into joint movements. However, the low decoding performance typically reached by the control system results in poor stability of the prosthetic device. In order to solve this issue, here we tested several state-of-the-art classifiers for decoding multi-joint hand movements from electromyographic recordings of arm muscles, collected from healthy subjects. Specifically, we tested: NonLinear Logistic Regression (NLR), Regularized Least-Square, Artificial Neural Network, Support Vector Machine, and Linear Discriminant Analysis. We aimed at minimizing the number of EMG electrodes (6 maximum) by optimizing each classifier in terms of the F1Score, and then we compared the performance of the classifiers. We found that the NLR algorithm achieved the best results with only 3 EMG electrodes. The optimized algorithms were then tested on three right arm amputees controlling a virtual hand. We obtained that algorithm’s performance was comparable with that obtained from healthy subjects. In particular, the NLR classifier achieved 99% correct classification for all the patients, indicating its potential effective use in prosthetic applications.

Working memory (WM) performance is very often measured using the n-back task, in which the participant is presented with a sequence of stimuli, and required to indicate whether the current stimulus matches the one presented n steps earlier. In this study, we used high-density electroencephalography (hdEEG) coupled to source localization to obtain information on spatial distribution and temporal dynamics of neural oscillations associated with WM update, maintenance and readout. Specifically, we a priori selected regions from a large fronto-parietal network, including also the insula and the cerebellum, and we analyzed modulation of neural oscillations by event-related desynchronization and synchronization (ERD/ERS).During update and readout, we found larger θ ERS and smaller β ERS respect to maintenance in all the selected areas. γLOWand γHIGHbands oscillations decreased in the frontal and insular cortices of the left hemisphere. In the maintenance phase we observed focally decreased θ oscillations and increased β oscillations (ERS) in most of the selected posterior areas and focally increased oscillations in γLOWand γHIGHbands in the frontal and insular cortices of the left hemisphere. Finally, during WM readout, we also found a focal modulation of the γLOWband in the left fusiform cortex and cerebellum, depending on the response trial type (true positive vs. true negative).Overall, our study demonstrated specific spectral signatures associated with updating of memory information, WM maintenance and readout, with relatively high spatial resolution.

Goal: Functional connectivity (FC) is an important indicator of the brain's state in different conditions, such as rest/task or health/pathology. Here we used high-density electroencephalography coupled to source reconstruction to assess frequency-specific changes of FC during resting state. Specifically, we computed the Small-World Propensity (SWP) index to characterize network small-world architecture across frequencies. Methods: We collected resting state data from healthy participants and built connectivity matrices maintaining the heterogeneity of connection strengths. For a subsample of participants, we also investigated whether the SWP captured FC changes after the execution of a working memory (WM) task. Results: We found that SWP demonstrates a selective increase in the alpha and low beta bands. Moreover, SWP was modulated by a cognitive task and showed increased values in the bands entrained by the WM task. Conclusions: SWP is a valid metric to characterize the frequency-specific behavior of resting state networks. ispartof: IEEE Open Journal of Engineering in Medicine and Biology vol:1 pages:57-64 ispartof: location:United States status: published

The correct human brain function is dependent on the activity of non-neuronal cells called astrocytes. The bioelectrical properties of astrocytes in vitro do not closely resemble those displayed in vivo and the former are incapable of generating action potential; thus, reliable approaches in vitro for noninvasive electrophysiological recording of astrocytes remain challenging for biomedical engineering. Here it is found that primary astrocytes grown on a device formed by a forest of randomly oriented gold coated-silicon nanowires, resembling the complex structural and functional phenotype expressed by astrocytes in vivo. The device enables noninvasive extracellular recording of the slow-frequency oscillations generated by differentiated astrocytes, while flat electrodes failed on recording signals from undifferentiated cells. Pathophysiological concentrations of extracellular potassium, occurring during epilepsy and spreading depression, modulate the power of slow oscillations generated by astrocytes. A reliable approach to study the role of astrocytes function in brain physiology and pathologies is presented.

One of the main limitations preventing the realization of a successful dialogue between the brain and a putative enabling device is the intricacy of brain signals. In this perspective, closed-loop in vitro systems can be used to investigate the interactions between a network of neurons and an external system, such as an interacting environment or an artificial device. In this chapter, we provide an overview of closed-loop in vitro systems, which have been developed for investigating potential neuroprosthetic applications. In particular, we first explore how to modify or set a target dynamical behavior in a network of neurons. We then analyze the behavior of in vitro systems connected to artificial devices, such as robots. Finally, we provide an overview of biological neuronal networks interacting with artificial neuronal networks, a configuration currently offering a promising solution for clinical applications.

Recent advances in the field of human brain imaging by electrophysiological recordings allow to reliably quantify the connectivity patterns of spontaneous oscillatory activity. This has provided novel tools to investigate the neural basis underlying complex human behavior and to unravel the mechanisms of brain function and reorganization in response to neurological diseases. In this context, we present a pipeline integrating high-density electroencephalography (hd-EEG) analysis and graph theory. To test our pipeline, we conducted a feasibility study on hd-EEG analysis to recordings from healthy individuals, examining the frequency-specific small-world organization of brain connectivity. Here we present the preliminary results on a small sample size. The ultimate goal of our study is to extract graph-theory related metrics, such as small-worldness index, from injured patients and use them as electrophysiological biomarkers of the recovery.

In the quest for novel neurotechnologies to defeat brain diseases, intelligent biohybrid systems have earned a privileged role among unconventional brain repair strategies. These systems are based on the functional interaction between the nervous tissue and engineered devices, the establishment of which is mediated by artificial intelligence. As novel, previously unimaginable neurotechnologies are emerging, what are the translational impact and the practical consequences carried by these tools for the clinical practice?In this review, we describe the progression of brain repair strategies, from the early pioneering demonstration of their feasibility to their recent implementation in the experimental and clinical settings. We will show how the convergence of different disciplines across the decades has led to the emergence of innovative concepts based on intelligent biohybrid designs. We discuss the advantages and limitations of the described approaches and we conclude by proposing possible solutions to the current shortcomings of available paradigms.

One of the main limitations preventing the realization of a successful dialogue between the brain and a putative enabling device is the intricacy of brain signals. In this perspective, closed-loop in vitro systems can be used to investigate the interactions between a network of neurons and an external system, such as an interacting environment or an artificial device. In this chapter, we provide an overview of closed-loop in vitro systems, which have been developed for investigating potential neuroprosthetic applications. In particular, we first explore how to modify or set a target dynamical behavior in a network of neurons. We then analyze the behavior of in vitro systems connected to artificial devices, such as robots. Finally, we provide an overview of biological neuronal networks interacting with artificial neuronal networks, a configuration currently offering a promising solution for clinical applications.

Intracortical microstimulation can be successfully used to manipulate neuronal activity and connectivity, thus representing a potentially powerful tool to steer neuroplasticity occurring after brain injury. Activity-dependent stimulation (ADS), in which the spikes recorded from a single neuron are used to trigger stimulation at another cortical location, is able to potentiate cortical connections between distant brain areas. Here, we developed an experimental procedure and a computational pipeline aimed at investigating the ability of ADS to induce changes in intra-cortical activity of healthy anesthetized rats.

The combination of recording and stimulation into closed-loop electrophysiological systems is one of the most challenging topics in neuroscience and neural engineering. The application of such systems can range from basic neuroscientific questions to neurorehabilitation purposes. In this review, we summarize the main results obtained with a hybrid neurorobotic platform that we developed in the past to perform closed-loop experiments. To minimize the complexity of the biological network, we used cultured neuronal networks kept alive over Micro Electrode Arrays. The robot is characterized by proximity sensors and wheels, allowing it to navigate within an arena with obstacles. The neurorobotic architecture can become an experimental environment for studying the interaction between brains and machines. We also discuss other possible applications of similar closed-loop architectures related to neuroprosthetics and neurorehabilitation.

Sensorimotor learning is a bidirectional process associated with concurrent neuroplastic changes in the motor and somatosensory system. While motor memory consolidation and retention have been extensively studied during skill acquisition, little is known about the formation and consolidation of somatosensory memory associated with motor learning. Using a robotic exoskeleton, we tracked markers of somatosensory and motor learning while healthy participants trained to make goal-directed wrist reaching movements over five days and evaluated retention for up to 10 days after practice. Markers of somatosensory learning were changes in wrist position sense bias (systematic error) and precision (random error). The main results are as follows: First, somatosensory (proprioceptive) memory consolidation shows signs of cost savings with repeated sensorimotor training - the same feature is known for motor memory formation. Moreover, somatosensory learning generalized to untrained workspace. Second, somatosensory learning over days can be characterized as an early improvement in sensory precision and a later improvement in sensory bias. Third, the time course of learning gains in position sense acuity coincided with improvements in spatial movement accuracy. Finally, the gains of somatosensory learning were retained for several days. Improvements in position sense bias were still visible up to 3 days after the end of practice for the trained workspace positions, but decayed faster in the untrained workspace. Improvements in position sense precision were retained for up to 10 days and were workspace independent. The findings are consistent with the view that an internal model of somatosensory joint space is formed during motor learning.

Motor brain-machine interfaces (BMIs) transform neural activities recorded directly from the brain into motor commands to control the movements of an external object by establishing an interface between the central nervous system (CNS) and the device. Bidirectional BMIs are closed-loop systems that add a sensory channel to provide the brain with an artificial feedback signal produced by the interaction between the device and the external world. Taking inspiration from the functioning of the spinal cord in mammalians, in our previous works we designed and developed a bidirectional BMI that uses the neural signals recorded form rats' motor cortex to control the movement of an external object. We implemented a decoding interface based on the approximation of a predefined force field with a central attractor point. Now we consider a non-linear transformation that allows to design a decoder approximating force fields with arbitrary attractors. We describe here the non-linear mapping algorithm and preliminary results of its use with behaving rats.

This study explored the psychophysical effects of intracortical microstimulation (ICMS) coupled to auditory stimulation during a behavioral detection task in rats. ICMS directed to the sensory areas of the cortex can be instrumental in facilitating operant conditioning behavior. Moreover, multisensory stimulation promotes learning by enabling the subject to access multiple information channels. However, the extent to which multisensory information can be used as a cue to make decisions has not been fully understood. This study addressed the exploration of the parameters of multisensory stimulation delivered to behaving rats in an operant conditioning task. Preliminary data indicate that animal decisions can be shaped by online changing the stimulation parameters.

This study examined the trainability of the proprioceptive sense and explored the relationship between proprioception and motor learning. With vision blocked, human learners had to perform goal-directed wrist movements relying solely on proprioceptive/haptic cues to reach several haptically specified targets. One group received additional somatosensory movement error feedback in form of vibro-tactile cues applied to the skin of the forearm. We used a haptic robotic device for the wrist and implemented a 3-day training regimen that required learners to make spatially precise goal-directed wrist reaching movements without vision. We assessed whether training improved the acuity of the wrist joint position sense. In addition, we checked if sensory learning generalized to the motor domain and improved spatial precision of wrist tracking movements that were not trained. The main findings of the study are: First, proprioceptive acuity of the wrist joint position sense improved after training for the group that received the combined proprioceptive/haptic and vibro-tactile feedback (VTF). Second, training had no impact on the spatial accuracy of the untrained tracking task. However, learners who had received VTF significantly reduced their reliance on haptic guidance feedback when performing the untrained motor task. That is, concurrent VTF was highly salient movement feedback and obviated the need for haptic feedback. Third, VTF can be also provided by the limb not involved in the task. Learners who received VTF to the contralateral limb equally benefitted. In conclusion, somatosensory training can significantly enhance proprioceptive acuity within days when learning is coupled with vibro-tactile sensory cues that provide feedback about movement errors. The observable sensory improvements in proprioception facilitates motor learning and such learning may generalize to the sensorimotor control of the untrained motor tasks. The implications of these findings for neurorehabilitation are discussed.

This paper reports a multi-channel neural spike recording system-on-chip with digital data compression and wireless telemetry. The circuit consists of 16 amplifiers, an analog time-division multiplexer, a single 8 bit analog-to-digital converter, a digital signal compression unit and a wireless transmitter. Although only 16 amplifiers are integrated in our current die version, the whole system is designed to work with 64, demonstrating the feasibility of a digital processing and narrowband wireless transmission of 64 neural recording channels. Compression of the raw data is achieved by detecting the action potentials (APs) and storing 20 samples for each spike waveform. This compression method retains sufficiently high data quality to allow for single neuron identification (spike sorting). The 400 MHz transmitter employs a Manchester-Coded Frequency Shift Keying (MC-FSK) modulator with low modulation index. In this way, a 1:25 Mbit/s data rate is delivered within a limited band of about 3 MHz. The chip is realized in a 0:35 µm AMS CMOS process featuring a 3 V power supply with an area of 3:1 x 2:7 mm2. The achieved transmission range is over 10 m with an overall power consumption for 64 channels of 17:2 mW. This figure translates into a power budget of 269 µW per channel, in line with published results but allowing a larger transmission distance and more efficient bandwidth occupation of the wireless link. The integrated circuit was mounted on a small and light board to be used during neuroscience experiments with freely-behaving rats. Powered by 2 AAA batteries, the system can continuously work for more than 100 hours allowing for long-lasting neural spike recordings.

The paper describes a multi-channel neural spike recording system sensing and processing the action potentials (APs) detected by an electrode array implanted in the cortex of freely-behaving small laboratory animals. The core of the system is a custom integrated circuit (IC), with low-noise analog front-end interfaced to a 16 electrode array followed by a single 8-bit SAR ADC, a digital signal compression and a 400-MHz wireless transmission units. Data compression is implemented by detecting action potentials and storing up to 20 points per each spike waveform. The choice greatly improves data quality and allows single spike identification. The transmitter delivers a 1.25-Mbit/s data rate coded with a Manchester-coded frequency shift keying (MC-FSK) within a 3-MHz bandwidth. An overall power consumption of 17.2mW makes possible to reach a transmission range larger than 20-m. The IC is mounted on a small and light printed circuit board. Two AAA batteries, set in a pack positioned on the back of the animal, power the system that can work continuously for more than 100h.

We present a novel experimental framework that implements a bidirectional brain-machine interface inspired by the operation of the spinal cord in vertebrates that generates a control policy in the form of a force field. The proposed experimental set-up allows connecting the brain of freely moving rats to an external device. We tested this apparatus in a preliminary experiment with an alert rat that used the interface for acquiring a food reward. The goal of this approach to bidirectional interfaces is to explore the role of voluntary neural commands in controlling a dynamical system represented by a small cart moving on vertical plane and connected to a water/pellet dispenser.

Brain–machine interface (BMI) systems establish a connection between the brain and artificial devices, such as a computer, a wheelchair, or a robotic arm, with the main goal of restoring lost sensory or motor functions in patients suffering from neurological injuries or diseases. A possible way of improving the performance of BMIs is to establish a two-way artificial bidirectional channel by integrating and exchanging online motor and sensory information in a closed-loop stream between the nervous system and the controlled device. In this chapter we first describe the essential components of a typical BMI providing an overview of the existing systems, and we then report a novel closed-loop bidirectional BMI developed in our laboratory.

Proprioception is central for motor control and its role must also be taken into account when designing motor rehabilitation training protocols. This is particularly important when dealing with motor deficits due to proprioceptive impairment such as peripheral sensory neuropathy. In these cases substituting or augmenting diminished proprioceptive sensory information might be beneficial for improving motor function. However it still remains to be understood how proprioceptive senses can be improved by training, how this would translate into motor improvement and whether additional sensory modalities during motor training contribute to the sensorimotor training process. This preliminary study investigated how proprioceptive/haptic training can be augmented by providing additional sensory information in the form of vibro-tactile feedback. We tested the acuity of the wrist proprioceptive position sense before and after robotic training in two groups of healthy subjects, one trained only with haptic feedback and one with haptic and vibro-tactile feedback. We found that only the group receiving the multimodal feedback significantly improved proprioceptive acuity. This study demonstrates that non-proprioceptive position feedback derived from another somatosensory modality is easily interpretable for humans and can contribute to an increased precision of joint position. The clinical implications of this finding will be outlined.

Neurological movement disorders such as stroke or sensory neuropathy are associated with somatosensory deficits. From a neurorehabilitation perspective, the assessment of proprioceptive function is important for planning and applying adequate rehabilitation treatments. Numerous behavioral and psychophysical methods are available to measure proprioceptive acuity. However, no universally accepted and adopted protocol exists. In recent years robotic devices have increasingly been used to investigate and assess proprioceptive function, but few studies have focused on the wrist joint. To fill this knowledge gap, this study aimed to systematically map the proprioceptive acuity of the wrist for its two joint degrees of freedom (DoF) - flexion/extension (FE) and abduction/adduction (AA). Twenty eight healthy young adults performed an ipsilateral, active joint position matching task using a robotic device. As a measure of proprioceptive acuity we determined the error between target position and the matched joint position. Results showed that: first, proprioceptive acuity varied between the two joint DoF with the matching error for AA being lower than the FE. Second, within each DoF, the motion direction did not affect the accuracy. Third, the radial component of the matching error showed DoF dependence: the FE movements tended to undershoot, while the AA movement overshot the target position. Results are indicative of a joint DoF dependent anisotropy of wrist proprioceptive acuity.

Upper limb robotic rehabilitation technology has recognized the importance of biofeedback signals for optimizing motor recovery. Impaired motor control is typically associated with abnormal muscle activation patterns and such patterns can be revealed by electromyography (EMG). However, before using user-generated EMG signals as biofeedback for controlling and optimizing force output of a robot, it is imperative that the healthy muscle innervation patterns are fully established to guide possible robot-aided rehabilitation protocols. Given the muscular redundancy found in many human limb systems, and given that force generation of human muscles is not linear, it becomes important to understand how synergistic control influences joint force and joint kinematics. It is the purpose of this study to map the electromyographic activation patterns of the major human muscles involved in controlling the hand/wrist system. By means of a robotic exoskeleton three Degrees of Freedom (DoF) were evaluated: wrist flexion-extension and adduction-abduction as well as forearm pronation-supination separately and for different ranges of the workspace. We recorded EMG activity of 7 arm muscles of 4 healthy subjects performing the task and we therefore defined the typical EMG activation patterns of such muscles during robotic training.

One of the most difficult tasks for the surgeon during the removal of low-grade gliomas is to identify as precisely as possible the borders between functional and non-functional brain tissue with the aim of obtaining the maximal possible resection which allows to the patient the longer survival. For this purpose, systems for acute extracellular recordings of single neuron and multi-unit activity are considered promising. Here we describe a system to be used with 16 microelectrodes arrays that consists of an autoclavable headstage, a built-in inserter for precise electrode positioning and a system that measures and controls the pressure exerted by the headstage on the brain with a twofold purpose: to increase recording stability and to avoid disturbance of local perfusion which would cause a degradation of the quality of the recording and, eventually, local ischemia. With respect to devices where only electrodes are autoclavable, our design permits the reduction of noise arising from long cable connections preserving at the same time the flexibility and avoiding long-lasting gas sterilization procedures. Finally, size is much smaller and set up time much shorter compared to commercial systems currently in use in surgery rooms, making it easy to consider our system very useful for intra-operatory mapping operations.

In the framework of developing new brain-machine interfaces, many valuable results have been obtained in understanding which features of neural activity can be used in controlling an external device. Somatosensory real-time feedback is crucial for motor planning and for executing "online" errors correction during the movement. In people with sensory motor disabilities cortical microstimulation can be used as sensory feedback to elicit an artificial sensation providing the brain with information about the external environment. Even if intracortical microstimulation (ICMS) is broadly used in several experiments, understanding the psychophysics of such artificial sensory channel is still an open issue. Here we present the results of a parametric study that aims to define which stimulation parameters are needed to create an artificial sensation. Behaving rats were trained to report by pressing a lever the presence of ICMS delivered through microwire electrodes chronically implanted in the barrel cortex. Psychometric curves obtained by varying pulse amplitude, pulse frequency and train duration, demonstrate that in freely moving animals the perception threshold of microstimulation increased with respect to previous studies with head-restrained rats.

Brain-Machine Interfaces (BMIs) are systems which mediate communication between brains and artificial devices. Their long term goal is to restore motor functions, and this ultimately demands the development of a new generation of bidirectional brain-machine interfaces establishing a two-way brain-world communication channel, by both decoding motor commands from neural activity and providing feedback to the brain by electrical stimulation. Taking inspiration from how the spinal cord of vertebrates mediates communication between the brain and the limbs, here we present a model of a bidirectional brain-machine interface that interacts with a dynamical system by generating a control policy in the form of a force field. In our model, bidirectional communication takes place via two elements: (a) a motor interface decoding activities recorded from a motor cortical area, and (b) a sensory interface encoding the state of the controlled device into electrical stimuli delivered to a somatosensory area. We propose a specific mathematical model of the sensory and motor interfaces guiding a point mass moving in a viscous medium, and we demonstrate its performance by testing it on realistically simulated neural responses.