Hybrid Brain Machine interface

A brain machine interface (BMI) is a system that directly decodes the user's intention from brain signals to control a robot or an external device or stimulator. These systems have shown to be potential tools for the rehabilitation of motor-impaired patients after stroke. Brain signals can be measur...

Enhancing motor rehabilitation

Gamification is a principle that introduces aspects of games into real-life environments. The areas in which it helps humans are manifold. Project and task management is only one example field that benefits from increased effectivity through gamification. Transforming rehabilitation into a game part...

Neural correlates of motor rehabilitation and statistical models

Uncovering brain processes that cooccur with or predict clinical motor improvement is an important building block for the development of stroke rehabilitation schemes (for chronic and severely paralyzed patients in particular). Only a comprehensive model of the patients can guarantee success in a pe...

Software development

Most of the software we use for signal processing and data analysis is developed in-house to meet high demands. Technologies employed at our institute range from high-level analysis in Matlab, statistics in R and machine learning in Python to lower-level software development for laboratory environme...

Sleep and motor learning

Sleep plays a major role in motor learning and consolidation. Previous studies have shown a direct relationship between brain signatures during sleep (i.e., spindles or slow waves) and motor learning (or other types of learning). In this context, brain-machine interfaces, in which we “select” where...

Closed-loop gait rehabilitation

This research line is focused on gait rehabilitation for patients with motor dysfunctions in lower limb. We aim to investigate and identify the neuroplasticity mechanisms occurring at the brain and spinal cord level which are responsible for motor recovery. Combining our broad knowledge in neuromodu...