Background
A brain–computer interface (BCI) is a system that measures brain activity and converts it in (nearly) real time into functionally useful outputs to replace, restore, enhance, supplement, or improve the natural outputs of the brain, thereby changing the ongoing interactions between the brain and its external or internal environments. It may additionally modify brain activity through targeted stimulation to create functionally useful inputs to the brain.
Over the past decades, BCIs have undergone rapid development, enabling achievements such as the control of robotic prostheses or the restoration of communication in individuals with severe motor impairments. BCIs are also increasingly used to support sensorimotor restoration and neurorehabilitation after neurological injury. In parallel, they have become powerful tools for studying how the brain encodes movement and how neural signals can be voluntarily modulated.
Recent advances in neural recording technologies have made it possible to investigate motor control at the level of neural populations. These studies show that neural activity often evolves within low-dimensional subspaces known as neural manifolds, which capture structured patterns of activity associated with different movements or movement phases. Understanding this organization has important implications for the development of more robust and stable BCIs.
Our focus
In our lab, we study brain–machine interfaces by combining the development of novel decoding approaches with the investigation of neural principles that can inform the design of more effective BCIs. A central objective of our work is the translational development of BCIs to restore sensorimotor function and support neurorehabilitation.
Our research spans both restorative and augmentative applications, including the restoration of motor function and sensation through bidirectional BCIs that integrate neural decoding with artificial sensory feedback.
Our projects use a range of neural recording techniques, from non-invasive approaches such as EEG to invasive recordings including sEEG, ECoG, and multi-unit activity, often in collaboration with clinical and research partners.
Current research directions include:
• Sensorimotor interactions in neural decoding:
Studying how motor commands and sensory feedback interact in neural activity, with the goal of improving decoding in bidirectional BCIs.
• High-resolution motor decoding:
Developing ECoG and sEEG-based BCIs capable of decoding fine motor actions such as individual finger movements.
• BCIs for neurorehabilitation:
Designing BCI paradigms that combine neural decoding with neurofeedback or robotic assistance to promote motor recovery.
• Motor augmentation using non-invasive BCIs:
Exploring how users can learn to control additional robotic effectors while preserving natural movement.
• Neural manifolds of motor states:
Studying neural representations of executed, imagined, and observed movements to improve BCI decoding.