Task-oriented rehabilitation is essential for hand function recovery in stroke patients, and recent advancements in BCI-controlled exoskeletons and neural biomarkers - such as post-movement beta rebound (PMBR) - offer new pathways to optimize these therapies. Movement-related EEG signals from the sensorimotor cortex, particularly PMBR (post-movement) and event-related desynchronization (ERD, during movement), exhibit high task specificity and correlate with stroke severity. This study evaluated PMBR in 34 chronic stroke patients across two cohorts, along with a control group of 16 healthy participants, during voluntary and exoskeleton-assisted movement tasks. Longitudinal tracking in the second cohort enabled the analysis of PMBR changes, with EEG recordings acquired at three timepoints over a 30-session rehabilitation program. Findings revealed significant PMBR alterations in both passive and active movement tasks: patients with severe impairment lacked a PMBR dipole in the ipsilesional hemisphere, while moderately impaired patients showed a diminished response. The marked differences in PMBR patterns between stroke patients and controls highlight the extent of sensorimotor cortex disruption due to stroke. ERD showed minimal task-specific variation, underscoring PMBR as a more reliable biomarker of motor function impairment. These findings support the use of PMBR, particularly the PMBR/ERD ratio, as a biomarker for EEG-guided monitoring of motor recovery over time during exoskeleton-assisted rehabilitation.

Conventional rehabilitation strategies for stroke survivors become difficult when voluntary movements are severely disturbed. Combining passive limb mobilization, robotic devices and EEG-based brain-computer interfaces (BCI) systems might improve treatment and clinical follow-up of these patients, but detailed knowledge of neurophysiological mechanisms involved in functional recovery, which might help for tailoring stroke treatment strategies, is lacking. Movement-related EEG changes (EEG event-related desynchronization (ERD) in [Formula: see text] and [Formula: see text] bands, an indicator of motor cortex activation traditionally used for BCI systems), were evaluated in a group of 23 paralyzed chronic stroke patients in two unilateral motor tasks alternating paretic and healthy hands ((i) passive movement, using a hand exoskeleton, and (ii) voluntary movement), and compared to nine healthy subjects. In tasks using unaffected hand, we observed an increase of contralesional hemisphere activation for stroke patients group. Unexpectedly, when using paralyzed hand, motor cortex activation was reduced or absent in severely affected group of patients, while patients with moderate motor deficit showed an activation greater than control group. Cortical activation was reduced or absent in damaged hemisphere of all the patients in both tasks. Significant differences related to severity of motor deficit were found in the time course of [Formula: see text]-[Formula: see text] bands power ratio in EEG of contralesional hemisphere while moving affected hand. These findings suggest the presence of different compensation mechanisms in contralesional hemisphere of stroke patients related to the grade of motor disability, that might turn quantitative EEG during a movement task, obtained from a BCI system controlling a robotic device included in a rehabilitation task, into a valuable tool for monitoring clinical progression, evaluating recovery, and tailoring treatment of stroke patients.

Modulation of sensorimotor rhythm (SMR) power, a rhythmic brain oscillation physiologically linked to motor imagery, is a popular Brain–Machine Interface (BMI) paradigm, but its interplay with slower cortical rhythms, also involved in movement preparation and cognitive processing, is not entirely understood. In this study, we evaluated the changes in phase and power of slow cortical activity in delta and theta bands, during a motor imagery task controlled by an SMR-based BMI system. In Experiment I, EEG of 20 right-handed healthy volunteers was recorded performing a motor-imagery task using an SMR-based BMI controlling a visual animation, and during task-free intervals. In Experiment II, 10 subjects were evaluated along five daily sessions, while BMI-controlling same visual animation, a buzzer, and a robotic hand exoskeleton. In both experiments, feedback received from the controlled device was proportional to SMR power (11–14[Formula: see text]Hz) detected by a real-time EEG-based system. Synchronization of slow EEG frequencies along the trials was evaluated using inter-trial-phase coherence (ITPC). Results: cortical oscillations of EEG in delta and theta frequencies synchronized at the onset and at the end of both active and task-free trials; ITPC was significantly modulated by feedback sensory modality received during the tasks; and ITPC synchronization progressively increased along the training. These findings suggest that phase-locking of slow rhythms and resetting by sensory afferences might be a functionally relevant mechanism in cortical control of motor function. We propose that analysis of phase synchronization of slow cortical rhythms might also improve identification of temporal edges in BMI tasks and might help to develop physiological markers for identification of context task switching and practice-related changes in brain function, with potentially important implications for design and monitoring of motor imagery-based BMI systems, an emerging tool in neurorehabilitation of stroke.

The paper presents the developing of a new robotic system for the administration of a highly sophisticated therapy to stroke patients. This therapy is able to maximize patient motivation and involvement in the therapy and continuously assess the progress of the recovery from the functional viewpoint. Current robotic rehabilitation systems do not include patient information on the control loop. The main novelty of the presented approach is to close patient in the loop and use multisensory data (such as pulse, skin conductance, skin temperature, position, velocity, etc.) to adaptively and dynamically change complexity of the therapy and real-time displays of a virtual reality system in accordance with specific patient requirements. First, an analysis of subject's physiological responses to different tasks is presented with the objective to select the best candidate of physiological signals to estimate the patient physiological state during the execution of a virtual rehabilitation task. Then, the design of a prototype of multimodal robotic platform is defined and developed to validate the scientific value of the proposed approach.

This paper presents a microdevice for the monitoring, control and management of electric loads at home. The key idea is to compact the electronic design as much as possible in order to install it inside a schuko socket. Moreover, the electronic schuko socket (electronic microdevice+schuko socket) has the feature of communicating with a central unit and with other microdevices over the existing powerlines. Using the existing power lines the proposed device can be installed in new buildings or the old ones. The main use of this device is to monitoring, control and management of electric loads to save energy and to prevent accidents produced by the different kind of devices (for instance, iron) used in domestic tasks.

This paper describes how the use of panoramic cameras can dramatically simplify safety issues for a robot arm moving in close proximity to human beings, since they can simultaneously observe a 360° field of view. We present in this context an approach to visual servoing in which both the manipulator as well as any other moving object are tracked. Reliability and robustness are enhanced by adaptative background modelling and global illumination change detection.

This paper concerns the continuity of vision based robot control law. Recently, we have proposed a way of allowing the temporary disappearance of image features during the control task based on weighted features. Futher more, we have redefined the camera invariant visual servoing approach in order to take into account the change of image features when zooming in or out during a positioning task. In this paper, we study ho to select some parameters of the weight function and we propose a local stability analysis of the invariant visual servoing approach with weighted features. Finally, experimental results demonstrate the improvements that can be achieved in the performance of vision-based control task.

In the recent past, the visibility problem in vision-based control has been widely investigated. The proposed solutions generally have a common goal: to always keep the object in the camera's field of view during the visual servoing. Contrary to this solution, we propose a new approach based on the concept of allowing the changes of visibility in image features during the control task. To this aim, the camera invariant visual-servoing approach has been redefined in order to take into account the changes of visibility in image features. A new smooth task function using weighted features is presented, and a continuous control law is obtained starting from it by imposing its exponential decrease to zero. Furthermore, the local stability analysis of the invariant visual-servoing approach with weighted features is presented. Finally, this promising way of dealing with the visibility issue has been successfully tested with an eye-in-hand robotic system.