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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. γLOW and γHIGH bands oscillations decreased in the frontal and insular cortices of the left hemisphere. In the maintenance phase we observed decreased θ oscillations and increased β oscillations (ERS) in most of the selected posterior areas and focally increased oscillations in γLOW and γHIGH bands in the frontal and insular cortices of the left hemisphere. Finally, during WM readout, we also found a focal modulation of the γLOW band 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., Specific spectral signatures are associated with updating of memory information, working memory maintenance, and readout in a large fronto‐parietal network, including also the insula and the cerebellum.

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.

Replacing the human hand with artificial devices of equal capability and effectiveness is a long-standing challenge. Even the most advanced hand prostheses, which have several active degrees of freedom controlled by the electrical signals of the stump's residual muscles, do not achieve the complexity, dexterity, and adaptability of the human hand. Thus, prosthesis abandonment rate remains high due to poor embodiment. Here, we report a prosthetic hand called Hannes that incorporates key biomimetic properties that make this prosthesis uniquely similar to a human hand. By means of an holistic design approach and through extensive codevelopment work involving researchers, patients, orthopaedists, and industrial designers, our proposed device simultaneously achieves accurate anthropomorphism, biomimetic performance, and human-like grasping behavior that outperform what is required in the execution of activities of daily living (ADLs). To evaluate the effectiveness and usability of Hannes, pilot trials on amputees were performed. Tests and questionnaires were used before and after a period of about 2 weeks, in which amputees could autonomously use Hannes domestically to perform ADLs. Last, experiments were conducted to validate Hannes's high performance and the human likeness of its grasping behavior. Although Hannes's speed is still lower than that achieved by the human hand, our experiments showed improved performance compared with existing research or commercial devices.

Non invasive brain stimulation is a widely used technique for several applications, generally aimed at modulating brain activity and thus behavior. While the behavioral effects can be monitored during the application of the stimulation, the electrophysiological correlates, such as electroencephalography (EEG), cannot, because the stimulation artifact dramatically affects the recorded signals. Here we addressed this problem and we analyzed the artifact that transcranial alternating current stimulation (tACS) leaves on EEG traces. We found that the stimulation noise adds itself non-linearly to the EEG signal and spectral analysis revealed a peak centered at the stimulation frequency. We then created a synthetic dataset by adding to real EEG traces numerically generated signals, matching the characteristics of the tACS artifact. We used this data to test a set of artifact removal techniques based on blind source separation (BSS) methods and wavelet decomposition and we found that the best performing technique is independent component analysis (ICA).

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.

Neurological diseases causing motor/cognitive impairments are among the most common causes of adult-onset disability. More than one billion of people are affected worldwide, and this number is expected to increase in upcoming years, because of the rapidly aging population. The frequent lack of complete recovery makes it desirable to develop novel neurorehabilitative treatments, suited to the patients, and better targeting the specific disability. To date, rehabilitation therapy can be aided by the technological support of robotic-based therapy, non-invasive brain stimulation, and neural interfaces. In this perspective, we will review the above methods by referring to the most recent advances in each field. Then, we propose and discuss current and future approaches based on the combination of the above. As pointed out in the recent literature, by combining traditional rehabilitation techniques with neuromodulation, biofeedback recordings and/or novel robotic and wearable assistive devices, several studies have proven it is possible to sensibly improve the amount of recovery with respect to traditional treatments. We will then discuss the possible applied research directions to maximize the outcome of a neurorehabilitation therapy, which should include the personalization of the therapy based on patient and clinician needs and preferences.

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.

The development of robotic devices for rehabilitation is a fast-growing field. Nowadays, thanks to novel technologies that have improved robots’ capabilities and offered more cost-effective solutions, robotic devices are increasingly being employed during clinical practice, with the goal of boosting patients’ recovery. Robotic rehabilitation is also widely used in the context of neurological disorders, where it is often provided in a variety of different fashions, depending on the specific function to be restored. Indeed, the effect of robot-aided neurorehabilitation can be maximized when used in combination with a proper training regimen (based on motor control paradigms) or with non-invasive brain machine interfaces. Therapy-induced changes in neural activity and behavioral performance, which may suggest underlying changes in neural plasticity, can be quantified by multimodal assessments of both sensorimotor performance and brain/muscular activity pre/post or during intervention. Here, we provide an overview of the most common robotic devices for upper and lower limb rehabilitation and we describe the aforementioned neurorehabilitation scenarios. We also review assessment techniques for the evaluation of robotic therapy. Additional exploitation of these research areas will highlight the crucial contribution of rehabilitation robotics for promoting recovery and answering questions about reorganization of brain functions in response to disease.

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.

Progress in decoding neural signals has enabled the development of interfaces that translate cortical brain activities into commands for operating robotic arms and other devices. The electrical stimulation of sensory areas provides a means to create artificial sensory information about the state of a device. Taken together, neural activity recording and microstimulation techniques allow us to embed a portion of the central nervous system within a closed-loop system, whose behavior emerges from the combined dynamical properties of its neural and artificial components. In this study we asked if it is possible to concurrently regulate this bidirectional brain-machine interaction so as to shape a desired dynamical behavior of the combined system. To this end, we followed a well-known biological pathway. In vertebrates, the communications between brain and limb mechanics are mediated by the spinal cord, which combines brain instructions with sensory information and organizes coordinated patterns of muscle forces driving the limbs along dynamically stable trajectories. We report the creation and testing of the first neural interface that emulates this sensory-motor interaction. The interface organizes a bidirectional communication between sensory and motor areas of the brain of anaesthetized rats and an external dynamical object with programmable properties. The system includes (a) a motor interface decoding signals from a motor cortical area, and (b) a sensory interface encoding the state of the external object into electrical stimuli to a somatosensory area. The interactions between brain activities and the state of the external object generate a family of trajectories converging upon a selected equilibrium point from arbitrary starting locations. Thus, the bidirectional interface establishes the possibility to specify not only a particular movement trajectory but an entire family of motions, which includes the prescribed reactions to unexpected perturbations., Author Summary Brain-machine interfaces establish new communication channels between the brain and the external world with the goal of restoring sensory and motor functions for people with severe paralysis or sensory impairments. Current methodologies are based on decoding the motor intent from the recorded neural activity and transforming the extracted information into motor commands to control external devices as robotic arms. We developed a novel computational approach, based on the concept of programming dynamical behaviors trough the bi-directional sensory-motor interaction between the brain and the connected external device. This approach is based on the emulation of some control features of a biological interface, the spinal cord. The first prototype of our interface controls the state of motion of a simulated point mass in a viscous medium. The position of the point mass is encoded into a stimulus to the somatosensory cortex of an anesthetized rat. The evoked activity of a population of motor cortical neurons is decoded into a force vector applied to the point mass. The parameters of the encoder and of the decoder are set to approximate a desired force field. In the first test of the interface, we obtained a family of trajectories that converged upon a stable attractor.

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.

We examine bidirectional brain-machine interfaces that control external devices in a closed loop by decoding motor cortical activity to command the device and by encoding the state of the device by delivering electrical stimuli to sensory areas. Although it is possible to design this artificial sensory-motor interaction while maintaining two independent channels of communication, here we propose a rule that closes the loop between flows of sensory and motor information in a way that approximates a desired dynamical policy expressed as a field of forces acting upon the controlled external device. We previously developed a first implementation of this approach based on linear decoding of neural activity recorded from the motor cortex into a set of forces (a force field) applied to a point mass, and on encoding of position of the point mass into patterns of electrical stimuli delivered to somatosensory areas. However, this previous algorithm had the limitation that it only worked in situations when the position-to-force map to be implemented is invertible. Here we overcome this limitation by developing a new non-linear form of the bidirectional interface that can approximate a virtually unlimited family of continuous fields. The new algorithm bases both the encoding of position information and the decoding of motor cortical activity on an explicit map between spike trains and the state space of the device computed with Multi-Dimensional-Scaling. We present a detailed computational analysis of the performance of the interface and a validation of its robustness by using synthetic neural responses in a simulated sensory-motor loop.