The use of electroencephalogram (EEG) and surface electromyography (sEMG) for acquiring neural activity and peripheral nerve activity has become popular due to their non-invasive manner, relative low-cost, portability, and easy implementation. In recent years, with the development of sensor technology, the equipment used for EEG and sEMG acquisition has been updated from cumbersome and immobile devices to small and portable ones, making it easier to integrate them with robots and rehabilitation devices. This brings the possibility of applying neural signal to control human assist robots and rehabilitation devices, such as exoskeleton robots, prosthesis, and manipulator, or fusing EEG and sEMG with the existing physical sensing of robot for a more natural human-computer interaction. The performance of assist devices on human body and the mechanism of neural change can also be explored by evaluating neural activity, so as to promote the improvement and optimization of human assist devices.
However, these new advantages also bring many new challenges. For example, sometimes the existing motor imagination paradigm is not directly related to the assist pattern of devices, how to design a new paradigm to control devices by human EEG? Another example is that the EEG signal is easily interfered with by noise, and noise from assist devices and body movement will be introduced in the human-robot coupled system. How do design filters for man-robot systems to obtain stable and effective neural electric signals? There are also many similar application problems in the aspects such as human-robot interaction system design, interaction mode design, and developing neural activity evaluation and signal decoding methods for new systems, etc. Despite recent advancements in technological design and evaluation, the ability of machines, robots, and artificial intelligence to interact with humans naturally and intuitively is still limited.
This Research topic will focus on the application of EEG and sEMG techniques in the broad field of robots, not only considering exoskeleton robots, prothesis, human assist robot but also the field of human-robot interaction. Therefore, This Research Topic welcomes submissions of Original Research and Review articles, along with Report, Data Report, Hypothesis & Theory, Methods, Mini Review, and Study Protocol but not limit to:
- EEG and EMG noise processing related to human-robot systems;
- EEG and EMG decoding algorithm;
- Application of EEG/sEMG
- Robot control based on neural electrical signals;
- Neuro-physical multimodal sensor fusion;
- Effect evaluation of robot system based on EEG/sEMG;
- Continuous motion estimation based on EEG and EMG;
We welcome both neuroscientific studies and human-robot interaction/cooperation system with techniques such as EEG/sEMG.
The use of electroencephalogram (EEG) and surface electromyography (sEMG) for acquiring neural activity and peripheral nerve activity has become popular due to their non-invasive manner, relative low-cost, portability, and easy implementation. In recent years, with the development of sensor technology, the equipment used for EEG and sEMG acquisition has been updated from cumbersome and immobile devices to small and portable ones, making it easier to integrate them with robots and rehabilitation devices. This brings the possibility of applying neural signal to control human assist robots and rehabilitation devices, such as exoskeleton robots, prosthesis, and manipulator, or fusing EEG and sEMG with the existing physical sensing of robot for a more natural human-computer interaction. The performance of assist devices on human body and the mechanism of neural change can also be explored by evaluating neural activity, so as to promote the improvement and optimization of human assist devices.
However, these new advantages also bring many new challenges. For example, sometimes the existing motor imagination paradigm is not directly related to the assist pattern of devices, how to design a new paradigm to control devices by human EEG? Another example is that the EEG signal is easily interfered with by noise, and noise from assist devices and body movement will be introduced in the human-robot coupled system. How do design filters for man-robot systems to obtain stable and effective neural electric signals? There are also many similar application problems in the aspects such as human-robot interaction system design, interaction mode design, and developing neural activity evaluation and signal decoding methods for new systems, etc. Despite recent advancements in technological design and evaluation, the ability of machines, robots, and artificial intelligence to interact with humans naturally and intuitively is still limited.
This Research topic will focus on the application of EEG and sEMG techniques in the broad field of robots, not only considering exoskeleton robots, prothesis, human assist robot but also the field of human-robot interaction. Therefore, This Research Topic welcomes submissions of Original Research and Review articles, along with Report, Data Report, Hypothesis & Theory, Methods, Mini Review, and Study Protocol but not limit to:
- EEG and EMG noise processing related to human-robot systems;
- EEG and EMG decoding algorithm;
- Application of EEG/sEMG
- Robot control based on neural electrical signals;
- Neuro-physical multimodal sensor fusion;
- Effect evaluation of robot system based on EEG/sEMG;
- Continuous motion estimation based on EEG and EMG;
We welcome both neuroscientific studies and human-robot interaction/cooperation system with techniques such as EEG/sEMG.
