Brain-Computer Interface (BCI) has advanced significantly in recent years in the field of robotics and artificial intelligence, where the direct connection between the human brain and robotic systems is connected for control and interaction. A BCI system includes both software and hardware modules, this state-of-the-art technology holds great potential for transforming various industries and applications, ranging from healthcare and rehabilitation to automation and assistive technologies. BCI technique decodes brain signals, translating them into actionable commands for robotic systems. Techniques such as electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), functional magnetic resonance imaging (fMRI), electromyography (EMG) and hybrid setups play a critical role in capturing the user's intentions, allowing for real-time control of robotic devices. The precision and speed of brain command decoding are fundamental factors influencing the responsiveness and effectiveness of brain-controlled robotics.
This Research Topic aims to present the integration of BCI, such as fMRI, fNIRS, EEG, EMG, wearable sensors and hybrid setups, with robotics to advance human-machine collaboration. The focus is on developing non-invasive systems that integrate neuroimaging BCI interfaces, enabling users to interact with robots and artificial intelligence in more natural and intuitive ways. The developments include novel techniques that leverage the complementary strengths of these modalities for improved real-time BCI setup, enhanced cognitive state recognition, and more efficient human-robot collaboration. Also to investigate the challenges, opportunities, and applications of this integrated approach in various domains, emphasizing the potential for improved user experience and performance.
This Research Topic welcomes any potential contribution to the understanding of how single and multiple modalities can be used to create more effective and adaptive BCI-based robotic systems. The research may address challenges in real-time data processing, feedback integration, and the design of autonomous robotic behaviors for interaction in various applications, from healthcare to industrial settings.
Keywords:
Brain-computer interface BCI, human machine collaboration, fMRI, EEG, fNIRS, EMG, hybrid-BCI, HCI, HMI, robotics, artificial intelligence, machine learning, deep learning, rehabilitation, assistive devices, exoskeletons, prosthetic, prosthesis, neurorobotics
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Brain-Computer Interface (BCI) has advanced significantly in recent years in the field of robotics and artificial intelligence, where the direct connection between the human brain and robotic systems is connected for control and interaction. A BCI system includes both software and hardware modules, this state-of-the-art technology holds great potential for transforming various industries and applications, ranging from healthcare and rehabilitation to automation and assistive technologies. BCI technique decodes brain signals, translating them into actionable commands for robotic systems. Techniques such as electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), functional magnetic resonance imaging (fMRI), electromyography (EMG) and hybrid setups play a critical role in capturing the user's intentions, allowing for real-time control of robotic devices. The precision and speed of brain command decoding are fundamental factors influencing the responsiveness and effectiveness of brain-controlled robotics.
This Research Topic aims to present the integration of BCI, such as fMRI, fNIRS, EEG, EMG, wearable sensors and hybrid setups, with robotics to advance human-machine collaboration. The focus is on developing non-invasive systems that integrate neuroimaging BCI interfaces, enabling users to interact with robots and artificial intelligence in more natural and intuitive ways. The developments include novel techniques that leverage the complementary strengths of these modalities for improved real-time BCI setup, enhanced cognitive state recognition, and more efficient human-robot collaboration. Also to investigate the challenges, opportunities, and applications of this integrated approach in various domains, emphasizing the potential for improved user experience and performance.
This Research Topic welcomes any potential contribution to the understanding of how single and multiple modalities can be used to create more effective and adaptive BCI-based robotic systems. The research may address challenges in real-time data processing, feedback integration, and the design of autonomous robotic behaviors for interaction in various applications, from healthcare to industrial settings.
Keywords:
Brain-computer interface BCI, human machine collaboration, fMRI, EEG, fNIRS, EMG, hybrid-BCI, HCI, HMI, robotics, artificial intelligence, machine learning, deep learning, rehabilitation, assistive devices, exoskeletons, prosthetic, prosthesis, neurorobotics
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.