About this Research Topic
Recent advances in human-robot interaction provide solutions for the restoration of sensory and motor function in patients with severe traumatic events as amputation, spinal cord injury, or stroke. Combining advanced robotic limb technology and neural interfaces it is today possible to recover significant levels of dexterity. Neuroprostheses further reduce negative side effects potentially increasing patients’ quality of life. Intraneural stimulation made it possible to restore sensory feedback, and consequently reduce phantom limb pain. Neuroprostheses are therefore a great alternative compared to commercially available prosthetic devices.
In the last decade, neuroprosthetic implementations have reached technical maturity to be used in patients' daily activity at home. This includes for example osseointegration of neural electrodes or robotic prosthesis for patients with amputation or fully integrated brain implants for Brain-machine interfaces among others. On the other hand, much scholarly literature has yet to evolve from their original proof-of-concept stage to viable solutions for clinical reality.
Our goal in this Research Topic is to identify the key aspects in making neuroprostheses available to a broad range of patients. Due to the complexity of neuroprosthetic devices, different aspects are treated:
A) The control of robotic limbs using myoelectric or neural recordings is very challenging in long-term applications. Especially the decoding of these physiological signals and their classification for robotic limb actuation is of great interest. Efficient decoding strategies are necessary for high degrees of freedom and to allow patients to naturally control their robotic limbs.
B) Sensory feedback based on robotic limb actuation is essential to restore a close-to-natural experience for robotic limb users. Complex sensors are therefore needed to translate robotic limb actuation to numerical data. Furthermore, biomimetic encoding strategies are necessary to translate this data to neural stimulation patterns. More compact systems are necessary including lightweight microcontrollers, stimulators, and a minimal amount of implanted recording or stimulation electrodes.
This Research Topic aims to present the major recent developments and future trends for human-machine interaction for neuroprosthetics and their translation to home-use. Areas to be covered in this Research Topic may include, but are not limited to:
• Clinical results showing improvement, facilitation of neuroprosthetics for home-use
• New electrode development to promote long-term functionality
• Improvement on signal stability, for recording or stimulation
• Systems with simplified donning and doffing
• Encoding and decoding strategies permitting reduction of cognitive-load,
• Encoding strategies for sensory feedback
• Wireless recording (ECoG, intraneural) or Neurostim system
• Decoding strategies for improved robotic limb control
• Sensor development to translate robotic limb actuation
• Consequent encoding strategies for neural stimulation
• Implantation strategies of EMG or neural electrodes
• Implantation strategies of a robotic prosthesis (e.g. osseointegrated devices)
• Implantation of microcontrollers and aspects related to the connection with the robotic limb, EMG and/or neural electrodes.
• Advanced and stable motor decoding via EMG
• Advanced and stable BMI decoding
This Research Topic has been realized in collaboration with Dr. Breanne Christie of John Hopkins University Applied Physics Laboratory, USA
The Research Topic image was designed by Freepik, adapted by Ivo Strauss
In the last decade, neuroprosthetic implementations have reached technical maturity to be used in patients' daily activity at home. This includes for example osseointegration of neural electrodes or robotic prosthesis for patients with amputation or fully integrated brain implants for Brain-machine interfaces among others. On the other hand, much scholarly literature has yet to evolve from their original proof-of-concept stage to viable solutions for clinical reality.
Our goal in this Research Topic is to identify the key aspects in making neuroprostheses available to a broad range of patients. Due to the complexity of neuroprosthetic devices, different aspects are treated:
A) The control of robotic limbs using myoelectric or neural recordings is very challenging in long-term applications. Especially the decoding of these physiological signals and their classification for robotic limb actuation is of great interest. Efficient decoding strategies are necessary for high degrees of freedom and to allow patients to naturally control their robotic limbs.
B) Sensory feedback based on robotic limb actuation is essential to restore a close-to-natural experience for robotic limb users. Complex sensors are therefore needed to translate robotic limb actuation to numerical data. Furthermore, biomimetic encoding strategies are necessary to translate this data to neural stimulation patterns. More compact systems are necessary including lightweight microcontrollers, stimulators, and a minimal amount of implanted recording or stimulation electrodes.
This Research Topic aims to present the major recent developments and future trends for human-machine interaction for neuroprosthetics and their translation to home-use. Areas to be covered in this Research Topic may include, but are not limited to:
• Clinical results showing improvement, facilitation of neuroprosthetics for home-use
• New electrode development to promote long-term functionality
• Improvement on signal stability, for recording or stimulation
• Systems with simplified donning and doffing
• Encoding and decoding strategies permitting reduction of cognitive-load,
• Encoding strategies for sensory feedback
• Wireless recording (ECoG, intraneural) or Neurostim system
• Decoding strategies for improved robotic limb control
• Sensor development to translate robotic limb actuation
• Consequent encoding strategies for neural stimulation
• Implantation strategies of EMG or neural electrodes
• Implantation strategies of a robotic prosthesis (e.g. osseointegrated devices)
• Implantation of microcontrollers and aspects related to the connection with the robotic limb, EMG and/or neural electrodes.
• Advanced and stable motor decoding via EMG
• Advanced and stable BMI decoding
This Research Topic has been realized in collaboration with Dr. Breanne Christie of John Hopkins University Applied Physics Laboratory, USA
The Research Topic image was designed by Freepik, adapted by Ivo Strauss
Keywords: human-machine interaction, neuroprosthetics, brain-machine interface (BMI), neural rehabilitation, neural interfaces, prosthetic home-use
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