The ambition of this project is to merge artificial intelligence and human intelligence in the control of brain-actuated robotic arms. Brain-computer interfaces (BCIs) can recognize human motion intention, so human intelligence is reflected by the BCI to control the robotic arm. The robotic arm is an autonomous system, which has artificial or machine intelligence based on vision servo control. For people suffering from severe neuromuscular disorders or accident injuries, a brain-controlled robotic arm is expected to provide assistance in their daily life. A primary bottleneck to achieve this objective is that the information transfer rate of current BCIs is not high enough to produce multiple and reliable commands during online robotic control. In this project, machine autonomy is infused into a BCI-controlled robotic arm system, where a user and a machine can work together to reach and grasp multiple objects in a given task. The intelligent robotic system can autonomously localize the potential targets and provide trajectory correction and grasping assistance accordingly. Meanwhile, the user only needs to complete rough reaching movement and target selection with a basic binary motor imagery based BCI, which can reduce the task difficulty and retain the volitional involvement of users at the same time.
BSc Biomedical Science, University of Birmingham.
MSc Computational Neuroscience and Cognitive Robotics, University of Birmingham.
Dr Dingguo Zhang
Dr Uriel Martinez Hernandez