We propose the development of a new technology for Non-Invasive Single Neuron Electrical Monitoring (NISNEM). Current non-invasive neuroimaging techniques including electroencephalography (EEG), magnetoencephalography (MEG) or functional magnetic resonance imaging (fMRI) provide indirect measures of the activity of large populations of neurons in the brain. However, it is becoming apparent that information at the single neuron level may be critical for understanding, diagnosing, and treating increasingly prevalent neurological conditions, such as stroke and dementia. Current methods to record single neuron activity are invasive - they require surgical implants. Implanted electrodes risk damage to the neural tissue and/or foreign body reaction that limit long-term stability. Understandably, this approach is not chosen by many patients; in fact, implanted electrode technologies are limited to animal preparations or tests on a handful of patients worldwide. Measuring single neuron activity non-invasively will transform how neurological conditions are diagnosed, monitored, and treated as well as pave the way for the broad adoption of neurotechnologies in healthcare. We propose the development of NISNEM by pushing frontier engineering research in electrode technology, ultra-low-noise electronics, and advanced signal processing, iteratively validated during extensive tests in pre-clinical trials. We will design and manufacture arrays of dry electrodes to be mounted on the skin with an ultra-high density of recording points. By aggressive miniaturization, we will develop microelectronics chips to record from thousands of channels with beyond state-of-art noise performance. We will devise breakthrough developments in unsupervised blind source identification of the activity of tens to hundreds of neurons from tens of thousands of recordings. This research will be supported by iterative pre-clinical studies in humans and animals, which will be essential for defining requirements and refining designs. We intend to demonstrate the feasibility of the NISNEM technology and its potential to become a routine clinical tool that transforms all aspects of healthcare. In particular, we expect it to drastically improve how neurological diseases are managed. Given that they are a massive burden and limit the quality of life of millions of patients and their families, the impact of NISNEM could be almost unprecedented. We envision the NISNEM technology to be adopted on a routine clinical basis for: 1) diagnostics (epilepsy, tremor, dementia); 2) monitoring (stroke, spinal cord injury, ageing); 3) intervention (closed-loop modulation of brain activity); 4) advancing our understanding of the nervous system (identifying pathological changes); and 5) development of neural interfaces for communication (Brain-Computer Interfaces for locked-in patients), control of (neuro)prosthetics, or replacement of a "missing sense" (e.g., auditory prosthetics). Moreover, by accurately detecting the patient's intent, this technology could be used to drive neural plasticity -the brain's ability to reorganize itself-, potentially enabling cures for currently incurable disorders such as stroke, spinal cord injury, or Parkinson's disease. NISNEM also provides the opportunity to extend treatment from the hospital to the home. For example, rehabilitation after a stroke occurs mainly in hospitals and for a limited period of time; home rehabilitation is absent. NISNEM could provide continuous rehabilitation at home through the use of therapeutic technologies. The neural engineering, neuroscience and clinical neurology communities will all greatly benefit from this radically new perspective and complementary knowledge base. NISNEM will foster a revolution in neurosciences and neurotechnology, strongly impacting these large academic communities and the clinical sector. Even more importantly, if successful, it will improve the life of millions of patients and their relatives

Robots can offer a solution to critical societal issues such as manpower shortage in hospitals, at home and in industries due to the aging population. Instead of replacing human workers (like conventional industrial robots), new robots should co-exist and collaborate with humans. Applications of human-robot interaction can be easily found in daily lives: robot-assisted rehabilitation, where the robot helps human patients complete a certain movement and regain various functions; collaborative object manipulation, where the robot carries an object together with its human partner and shares the object load; and semi-autonomous driving, where the vehicle's controller (robot) shares the control of the vehicle with the human driver and provides assistance to them in tasks such as line following and obstacle avoidance. The interaction behaviours in these applications range from collaboration, co-operation to competition: Application 1 (co-operation and competition): in robot-assisted rehabilitation, a robot should provide assistance to the patient when they could not complete a task by themselves; it should reduce its assistance to and even challenge the patient to promote their learning according to their recovery progress. Application 2 (collaboration): in collaborative object manipulation, a robot and its human partner have the same target position to reach but they may have different motion plans due to their individual local sensing of the environment, so the robot should consider the human partner's motion intention when planning its own motion and possibly the human also adapts to the robot's behaviour. Application 3 (co-operation and collaboration): in semi-autonomous driving, a robot should be able to complete a well-defined task, e.g. track a lane in normal conditions and when necessary allow the human driver to take correcting actions by shared control of steering, e.g. changing to a new road. This project will develop a unified framework to analyse these different interaction behaviours, and more importantly, will design a robot controller to achieve natural and efficient human-robot interaction. Differential game theory, which has been proved to be powerful in modelling multi-agent systems, is a suitable choice to categorize interaction between a human and a robot. However, how it can be used to develop a robot controller that efficiently responds to its human partner needs to be investigated. Two fundamental problems will be addressed: how to continuously identify the human partner's motion planning through haptic information and how to update the robot's control strategy to ensure a desired interaction. In this project, identification techniques will be employed to estimate the partner's motion planning and control theory will be used to develop a stable and optimal robot controller. A targeted benchmark system of robot-assisted physical training will be developed to test and illustrate the power of the proposed approach in improving the training system and predicting human behaviours. We envision that the game theory robotic controller will enable human users to interact with a robot as intuitively and efficiently as with a human, since the robot will adapt its behaviour to the human partner according to the context of the task. This project promises breakthroughs in human-robot interaction.