Recent years have witnessed a growing interest in lower limb exoskeletons (LLEs) designed to restore walking capabilities for individuals with motor impairments. Currently, the research community faces the challenge of enhancing the translational impact of this technology by proposing solutions to move from laboratory and clinical settings to daily life scenarios. In this context, the topic of environment-adaptive gait planning (EAGP) is of particular interest and importance. EAGP refers to the ability of LLEs to assist in walking on various terrains (e.g., stairs and slopes) and under different conditions (e.g., the presence of obstacles and uneven ground) by adapting their behavior according to the context. However, the proliferation of publications has led to a general lack of convergence within the scientific community toward a common definition of this research topic. Therefore, this article proposes a review of EAGP for LLEs. The objectives are to: 1) provide a clear definition of EAGP, highlighting its main components and characteristics; 2) propose a taxonomy for classifying the most frequently used techniques in the state of the art; and 3) conduct a comprehensive survey of the literature, situating each article within the proposed taxonomy. In addition, we identify the most urgent future challenges toward the development of more autonomous LLEs.

Covert visuospatial attention (CVSA) enables users to direct focus without eye movements, offering a promising non-invasive control modality for brain-machine interface (BMI) systems. Traditional CVSA-based BMIs rely on decoding α-band electroencephalography (EEG) activity from whole-trial data, often overlooking the temporal dynamics of attentional processing. In this study, we propose a novel control framework that explicitly separates the CVSA task into two subcomponents: shift attention and sustained attention. EEG signals were recorded from nine participants performing a CVSA task in a real-world environment. Through spectral feature analysis and temporal segmentation, we identify distinct patterns associated with each subcomponent and train dedicated classifiers accordingly. Our pseudo-online analysis suggests that combining the outputs of two task-specific classifiers significantly improves both trial-level classification accuracy (by an average of +23.74%) and area under the curve (AUC) (by +4.49%) compared to the conventional approach. These findings suggest that modelling the temporal structure of CVSA can enhance decoding performance and offer a more robust foundation for attention-based BMI.

Safe and effective powered wheelchair (PW) use requires training, which is typically conducted in clinical settings with specific clinical evaluation methods, such as the Powered Mobility Program (PMP). However, these methods lack objective performance assessment.Virtual Reality (VR) has emerged as a promising solution for safe, home-based training, also reducing the high costs for healthcare systems. Still, most existing VR-based solutions often rely on joystick or hand-tracking controls, making them inaccessible to individuals with severe upper limb impairments. To address this gap, we developed VR-PMP, a VR-based wheelchair driving simulator integrated with a hybrid Brain-Computer Interface (BCI) that combines electroencephalography (EEG) and electrooculography (EOG) signals for control. This system targets individuals with severe motor impairments but preserved cognitive function who could benefit from alternative, non-manual control methods. In this proof-of-concept study, we aimed to evaluate the feasibility of such a BCI system in a group of six adults without disabilities.Results showed that participants successfully navigated the virtual environment using the EOG-EEG BCI, achieving up to 100% accuracy in the classifier evaluation phase. These findings highlight the potential of our system as an innovative, alternative access solution for powered wheelchair mobility, paving the way for future clinical applications.

Motor imagery (MI) is a fundamental brain-machine interface (BMI) paradigm in which users learn how to modulate their brain signals to voluntarily and selectively activate specific areas of the sensorimotor cortex. The self-paced nature of this kinesthetic imagination makes MI well-suited for several human-robot interaction (HRI) scenarios. However, the performance of MI decoding is highly dependent on both the user’s expertise and the quality of the decoding algorithm. To address these challenges, we propose a method that integrates environmental data from robotic sensors to enhance the MI decoding process. Preliminary experiments indicate that this approach improves decoding accuracy and the overall performance of the brain-driven system, opening new opportunities for research on how machines can enhance the usability of BMIs systems.

Powered lower limb exoskeletons (LLEs) have emerged as wearable robots designed to augment users’ locomotion capabilities, offering mechanical support and additional power for both healthy and impaired subjects. However, current assistive exoskeletons are limited by predefined motion trajectories, hindering adaptability to unstructured environments encountered in daily life. To address this limitation, this paper proposes an environment-adaptive gait planning (EAGP) solution. The approach integrates scene understanding, pose estimation, and adaptive gait planning modules. A novel Collision-Free Foot Trajectory Generator (CFFTG) algorithm facilitates obstacle avoidance by computing collision-free foot trajectories, enhancing safety and adaptability. Through inverse kinematics, the planned trajectories are converted into angular joint trajectories for execution by low-level control. This comprehensive framework aims to enhance the adaptability and safety of LLEs, paving the way for broader real-world applications beyond clinical and research settings.

In this paper, we present a control system for the continuous teleoperation of a robotic manipulator via brain-machine interface (BMI). The proposed solution is based on shared control approach that allows the user to only focus on the operational tasks, while the low-level control details are automatically handled by the robotic intelligence. The user drives the manipulator through the imagination of limb movements (both hands vs. both feet) and a parameterized mapping function is implemented to convert the continuous BMI outputs into robot velocity commands which are sent to the shared control framework. The latter consists in: (i) a target predictor module, to infer the most probable target objects from the sequence of BMI commands; (ii) a control module based on an improved version of artificial potential fields (APF) to assist the user in reaching the target while avoiding collisions with obstacles in the environment. The system has been tested with a sample subject in a tabletop reach-to-grasp experiment with multiple target objects and obstacles achieving a success rate of 80%. The proposed system could be used in the future to help people with severe motor disabilities in performing daily life operations, such as drinking, feeding or manipulating objects.

This article proposes a novelshared intelligencesystem for brain-machine interface (BMI) teleoperated mobile robots where user’s intention and robot’s intelligence are concurrent elements equally participating in the decision process. We designed the system to rely onpoliciesguiding the robot’s behavior according to the current situation. We hypothesized that the fusion of thesepolicieswould lead to the identification of the next, most probable, location of the robot in accordance with the user’s expectations. We asked 13 healthy subjects to evaluate the system during teleoperated navigation tasks in a crowded office environment with a keyboard (reliable interface) and with 2-class motor imagery (MI) BMI (uncertain control channel). Experimental results show that ourshared intelligencesystem 1) allows users to efficiently teleoperate the robot in both control modalities; 2) it ensures a level of BMI navigation performances comparable to the keyboard control; 3) it actively assists BMI users in accomplishing the tasks. These results highlight the importance of investigating advanced human-machine interaction (HMI) strategies and introducing robotic intelligence to improve the performances of BMI actuated devices.

This work proposes an overview of the recent applications of brain-computer interface (BCI) technology for pediatric populations. Current BCIs have demonstrated the possibility to provide an alternative communication and interaction channel for people suffering from severe motor disabilities. However, to date research has been predominantly conducted in adults, only a few systems have been applied to pediatric population. A survey was carried out to show the ongoing trends of using BCI systems with children. We discuss three areas of applications where BCI might be helpful to children - "Communication & Control", "BCI Gaming for Neurofeedback Training" and "Rehabilitation" - highlighting the current limitations and the possible future challenges.

The increasing interest of the research community in the intertwined fields of brain-machine interface (BMI) and robotics has led to the development of a variety of brain-actuated devices, ranging from powered wheelchairs and telepresence robots to wearable exoskeletons. Nevertheless, in most cases, the interaction between the two systems is still rudimentary, allowing only an unidirectional simple communication from the BMI to the robot that acts as a mere passive end-effector. This limitation could be due to the lack of a common research framework, facilitating the integration of these two technologies. In this scenario, we proposed ROS-Neuro to overcome the aforementioned limitations by providing a common middleware between BMI and robotics. In this work, we present a working example of the potentialities of ROS-Neuro by describing a full closed-loop implementation of a BMI based on motor imagination. The paper shows the general structure of a closed-loop BMI in ROS-Neuro and describes the specific implementation of the packages related to the proposed motor imagery BMI, already available online with source codes, tutorials and documentations. Furthermore, we show two practical case scenarios where the implemented BMI is used to control a computer game or a telepresence robot with ROS-Neuro. We evaluated the performance of ROS-Neuro by ensuring comparable results with respect to a previous BMI software already validated. Results demonstrated the correct behavior of the provided packages.

Recently, the complexity analysis of brain activity has shown the possibility to provide additional information to discriminate between rest and motion in real-time. In this work, we propose a novel entropy-based machine learning method to classify between standing and walking conditions from the sole brain activity. The Shannon entropy has been used as a complexity measure of electroencephalography (EEG) signals and subject-specific features for classification have been selected by Common Spatial Patterns (CSP) filter. Exploiting these features with a linear classifier, we achieved > 85% of classification accuracy over a long period (≈ 25 min) of standing and treadmill walking on 11 healthy subjects. Moreover, we implemented the proposed approach to successfully discriminate in real-time between standing and over-ground walking on one healthy subject. We suggest that the reliable discrimination of rest against walking conditions achieved by the proposed method may be exploited to have more stable control of devices to restore locomotion, avoiding unpredictable and dangerous behaviors due to the delivery of undesired control commands.

Despite the growing interest in brain-machine interface (BMI)-driven neuroprostheses, the translation of the BMI output into a suitable control signal for the robotic device is often neglected. In this article, we propose a novel control approach based on dynamical systems that was explicitly designed to take into account the nature of the BMI output that actively supports the user in delivering real-valued commands to the device and, at the same time, reduces the false positive rate. We hypothesize that such a control framework would allow users to continuously drive a mobile robot and it would enhance the navigation performance. 13 healthy users evaluated the system during three experimental sessions. Users exploit a 2-class motor imagery BMI to drive the robot to five targets in two experimental conditions: with a discrete control strategy, traditionally exploited in the BMI field, and with the novel continuous control framework developed herein. Experimental results show that the new approach: 1) allows users to continuously drive the mobile robot via BMI; 2) leads to significant improvements in the navigation performance; and 3) promotes a better coupling between user and robot. These results highlight the importance of designing a suitable control framework to improve the performance and the reliability of BMI-driven neurorobotic devices.

Recent advances in the Brain-Machine Interface (BMI) and in the robotic fields allowed researchers to design a new generation of brain-actuated neuroprostheses to control a variety of assistive devices-ranging from powered wheelchairs to telepresence robots and robotic arms. However, the current interaction between BMI and robotic applications is still at its infancy. One of the reasons for the limited integration between these two technologies might be identified in the lack of a common research framework. In this scenario, ROS-Neuro may represent a solution to this challenge by providing a common middleware between BMI and robotics.In this work, we propose two packages for the ROS-Neuro middleware to acquire neural signals from different commercial acquisition devices and to store them into common data formats. We describe the usage and the main functionalities of both packages such as the provided nodes, the published and subscribed topics and the available services. Furthermore, the packages are available online together with source codes, tutorials and documentations. We evaluated the performances of the two packages to ensure the absence of delays or missing data with three commercial acquisition systems and in different operating configurations. Results demonstrated the correct behavior of the provided packages. The acquisition and recorder packages represent the first releases in the ROS-Neuro ecosystem and they aim at boosting both the shared development and the research activity in the multidisciplinary field of brain-actuated robots.

The identification of intentionally delivered commands is a challenge in Brain Computer Interfaces (BCIs) based on Sensory-Motor Rhythms (SMR). It is of fundamental importance that BCI systems controlling a robotic device (i.e., upper limb prosthesis) are capable of detecting if the user is in the so called Intentional Non-Control (INC) state (i.e., holding the prosthesis in a given position). In this work, we propose a novel approach based on the entropy of the Electroencephalogram (EEG) signals to provide a continuous identification of motion intention. Results from ten healthy subjects suggest that the proposed system can be used for reliably predicting motion in real-time at a framerate of 8 Hz with 80%±5% of accuracy. Moreover, motion intention can be detected more than 1 second before muscular activation with an average accuracy of 76%±11%.

This paper shows and evaluates a novel approach to integrate a non-invasive Brain-Computer Interface (BCI) with the Robot Operating System (ROS) to mentally drive a telepresence robot. Controlling a mobile device by using human brain signals might improve the quality of life of people suffering from severe physical disabilities or elderly people who cannot move anymore. Thus, the BCI user can actively interact with relatives and friends located in different rooms thanks to a video streaming connection to the robot. To facilitate the control of the robot via BCI, we explore new ROS-based algorithms for navigation and obstacle avoidance in order to make the system safer and more reliable. In this regard, the robot exploits two maps of the environment, one for localization and one for navigation, and both are used as additional visual feedback for the BCI user to control the robot position. Experimental results show a decrease of the number of commands needed to complete the navigation task, suggesting a reduction user's cognitive workload. The novelty of this work is to provide a first evidence of an integration between BCI and ROS that can simplify and foster the development of software for BCI driven robotics devices.

This paper presents an important step forward towards increasing the independence of people with severe motor disabilities, by using brain-computer interfaces to harness the power of the Internet of Things. We analyze the stability of brain signals as end-users with motor disabilities progress from performing simple standard on-screen training tasks to interacting with real devices in the real world. Furthermore, we demonstrate how the concept of shared control-which interprets the user's commands in context-empowers users to perform rather complex tasks without a high workload. We present the results of nine end-users with motor disabilities who were able to complete navigation tasks with a telepresence robot successfully in a remote environment (in some cases in a different country) that they had never previously visited. Moreover, these end-users achieved similar levels of performance to a control group of 10 healthy users who were already familiar with the environment.

This paper discusses and evaluates the role of shared control approach in a BCI-based telepresence framework. Driving a mobile device by using human brain signals might improve the quality of life of people suffering from severely physical disabilities. By means of a bidirectional audio/video connection to a robot, the BCI user is able to interact actively with relatives and friends located in different rooms. However, the control of robots through an uncertain channel as a BCI may be complicated and exhaustive. Shared control can facilitate the operation of brain-controlled telepresence robots, as demonstrated by the experimental results reported here. In fact, it allows all subjects to complete a rather complex task, driving the robot in a natural environment along a path with several targets and obstacles, in shorter times and with less number of mental commands.

Brain computer interface is a system that offers also a support to the patients with neuromuscular diseases as amyotrophic lateral sclerosis.In this paper are presented some works with the aim to integrate brain computer interfaces and mobile robots.The two aim of this project are: (i) to test an improved BCI experience through the help of a physical robot, so that brain signals are stronger stimulate. (ii) to use a remote robot controlled by a highly paralyzed patient via a BCI through a friendly graphic user. Some preliminary experiments are presented in this paper about one of the possible application: a robotic museum guide(PeopleBot and Pioneer3 robot), that can transmit remote visual perceptions to the patient.