NameBrief DescriptionDurationFunder


"Decoding the Neural Code of Human Movements for a New Generation of Man-machine Interfaces"


The generation of a movement is the combination of discrete events (action potentials) generated in the brain, spinal cord, nerves, and muscles. The ensemble of spike trains discharged in the various parts of the neuromuscular system constitutes the neural code for movements. Recording and interpretation of this code provides the means for decoding the motor system.

Under this project, we developed advanced electrode systems for in-vivo electrophysiological recordings from nerves and muscles in humans and new computational methods/models for extracting functionally significant information on human movement from these recordings.

 2011 - 2016   ERC (H2020)


"Advanced Myoelectric Control of Prosthetic Systems"


In spite of decades of research and many capabilities and potentials as well as incremental improvements, advanced human-machine interfaces based on the electromyogram (EMG) still have a significant distance from professional and commercial applications. This is also and particularly true for myoelectric prosthetic devices. Available commercial myoelectric control systems for prostheses can only control one single degree-of-freedom at a time.

Under this project, we developed (1) new signal acquisition methods based on HD-EMG electrodes and silicone based electrodes; and (2) advanced signal processing methods based on neurophysiological modelling and machine learning principles to achieve intuitive, in order to achieve proportional and simultaneous control of two DOF. Furthermore, we developed a novel training system based on performance and psychometric measures.

 2010 - 2014  ERC (H2020)




Biological signals recorded from the human body can be translated into actions of external devices to create man-machine interaction. Among the possible biosignals for man-machine interaction, muscle signals, i.e. electromyography (EMG), are the only that allow applications in routine clinical use within a commercially reasonable time horizon. However, current commercially viable myoelectric interfaces do not implement sensory-motor integration (decoding intentions and at the same time providing a sensory feedback to the patient), which conversely is the basis of plasticity of the central nervous system.

The aim of MYOSENS in the prosthetic area was to develop clinical systems that allow transmitting information to the prosthetic user on the status of the controlled artificial limb (e.g., on the force exerted), in order to close the control loop. MYOSENS addressed this problem in a systematic way by analysing all parts of the problem, from the definition of the best feedback variables to the way in which these variables could be communicated to the users, and to clinical tests that indicated how feedback was used by patients.

 2012 - 2016  ERC (H2020)
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