In the last few decades, scientific, medical, and industrial research communities have shown a greater interest in the field of Brain-Robot Interfaces (BRIs). These systems are designed to decode the user's intentions by recognizing specific task-related neural patterns and translate them into intelligent actions performed by external robotic devices such as wheelchairs, telepresence robots, exoskeletons, and robotic arms. Despite the substantial improvements achieved by recent BRI systems, neurorobots driven by the sole brain activity are not fully acceptable in both clinical and home-care settings, due to their low reliability, low accuracy, and low informative content. Moreover, the workload required to both healthy and end-users is still high, slowing down the expansion of these neurorobotic systems and their translational impact.
Following recent attempts to overcome these limitations, hybrid brain-robot interface (h-BRI) technologies have been proposed to provide a more robust input to neurorobotic devices and to extend their use on a wider population of patients. These solutions consist of the combination and parallel usage of a BRI system with at least one additional interface using either (neuro)physiological signals (e.g., electromyography, electrooculography) or other communication means (e.g., joystick, inertial sensors, cameras). Opportunities are abundant, such as fusing the multiple biosignals to enhance the accuracy of conventional BRI or using the different interfaces to increase the degree of control over the robotic devices. Hybrid interfaces may also be useful in rehabilitation, as one interface could be used for control purposes, while the others to provide clinicians with a more complete overview of the patients’ neurophysiological state (e.g., monitor user’s fatigue, workload, or engagement), or to drive neural-based sensory feedback enhancing motor learning and functional recovery.
This Research Topic seeks original manuscripts describing new research in the field of hybrid brain-robot and human-machine interfaces. The papers should include at least one BRI in their hybrid approach. All categories of BRI, i.e., based on invasive (ECoG, subcortical electrode arrays) or non-invasive (Electroencephalography – EEG, functional Magnetic Resonance Imaging – fMRI) recording modalities, are welcome. Methods adopting some of the interfaces to monitor the user state or to provide invasive/non-invasive sensory feedback are also of interest. Methodological papers, reviews, and theoretical contributions are also welcome. Areas of Interest:
- h-BRI for robotic control
- h-BRI control of prosthetic limbs
- h-BRI control of exoskeletons
- h-BRI and hybrid assistive technologies (e.g., exoskeleton + functional electrical stimulation – FES)
- h-BRI and shared-control approaches
- Neuroergonomics using h-BRI
- h-BRI and sensory feedback
- h-BRI and embodiment
- h-BRI in robot-aided rehabilitation
- h-BRI in industry 4.0
- Software and/or hardware platforms for h-BRI
We would like to extend special thanks to the coordinator of this Research Topic Dr. Stefano Tortora, who is currently affiliated with the University of Padova in Padova, Italy. His research interests include hybrid human-machine interfaces and neurorobotics.
In the last few decades, scientific, medical, and industrial research communities have shown a greater interest in the field of Brain-Robot Interfaces (BRIs). These systems are designed to decode the user's intentions by recognizing specific task-related neural patterns and translate them into intelligent actions performed by external robotic devices such as wheelchairs, telepresence robots, exoskeletons, and robotic arms. Despite the substantial improvements achieved by recent BRI systems, neurorobots driven by the sole brain activity are not fully acceptable in both clinical and home-care settings, due to their low reliability, low accuracy, and low informative content. Moreover, the workload required to both healthy and end-users is still high, slowing down the expansion of these neurorobotic systems and their translational impact.
Following recent attempts to overcome these limitations, hybrid brain-robot interface (h-BRI) technologies have been proposed to provide a more robust input to neurorobotic devices and to extend their use on a wider population of patients. These solutions consist of the combination and parallel usage of a BRI system with at least one additional interface using either (neuro)physiological signals (e.g., electromyography, electrooculography) or other communication means (e.g., joystick, inertial sensors, cameras). Opportunities are abundant, such as fusing the multiple biosignals to enhance the accuracy of conventional BRI or using the different interfaces to increase the degree of control over the robotic devices. Hybrid interfaces may also be useful in rehabilitation, as one interface could be used for control purposes, while the others to provide clinicians with a more complete overview of the patients’ neurophysiological state (e.g., monitor user’s fatigue, workload, or engagement), or to drive neural-based sensory feedback enhancing motor learning and functional recovery.
This Research Topic seeks original manuscripts describing new research in the field of hybrid brain-robot and human-machine interfaces. The papers should include at least one BRI in their hybrid approach. All categories of BRI, i.e., based on invasive (ECoG, subcortical electrode arrays) or non-invasive (Electroencephalography – EEG, functional Magnetic Resonance Imaging – fMRI) recording modalities, are welcome. Methods adopting some of the interfaces to monitor the user state or to provide invasive/non-invasive sensory feedback are also of interest. Methodological papers, reviews, and theoretical contributions are also welcome. Areas of Interest:
- h-BRI for robotic control
- h-BRI control of prosthetic limbs
- h-BRI control of exoskeletons
- h-BRI and hybrid assistive technologies (e.g., exoskeleton + functional electrical stimulation – FES)
- h-BRI and shared-control approaches
- Neuroergonomics using h-BRI
- h-BRI and sensory feedback
- h-BRI and embodiment
- h-BRI in robot-aided rehabilitation
- h-BRI in industry 4.0
- Software and/or hardware platforms for h-BRI
We would like to extend special thanks to the coordinator of this Research Topic Dr. Stefano Tortora, who is currently affiliated with the University of Padova in Padova, Italy. His research interests include hybrid human-machine interfaces and neurorobotics.
