Project Database
This page contains the database of possible research projects for master and bachelor students in the Biorobotics Laboratory (BioRob). Visiting students are also welcome to join BioRob, but it should be noted that no funding is offered for those projects (see https://biorob.epfl.ch/students/ for instructions). To enroll for a project, please directly contact one of the assistants (directly in his/her office, by phone or by mail). Spontaneous propositions for projects are also welcome, if they are related to the research topics of BioRob, see the BioRob Research pages and the results of previous student projects.
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Amphibious robotics
Computational Neuroscience
Dynamical systems
Human-exoskeleton dynamics and control
Humanoid robotics
Miscellaneous
Mobile robotics
Modular robotics
Neuro-muscular modelling
Quadruped robotics
Computational Neuroscience
| 755 – High-performance enconder-decoder design for computational neural signal processing |
| Category: | semester project, master project (full-time), internship | |
| Keywords: | Computational Neuroscience, Data Processing, Linux, Programming, Python | |
| Type: | 20% theory, 5% hardware, 75% software | |
| Responsible: | (MED11626, phone: 41783141830) | |
| Description: | Background Brain-computer interfaces (BCIs) using signals acquired with intracortical implants have achieved successful high-dimensional robotic device control useful for completing daily tasks. However, the substantial amount of medical and surgical expertise required to correctly implant and operate these systems greatly limits their use beyond a few clinical cases. A non-invasive counterpart requiring less intervention that can provide high-quality control would profoundly improve the integration of BCIS into multiple settings, and represent a nascent research field, brain robotics. However, this is challenging due to the inherent complexity of neural signals and difficulties in online neural decoding with efficient algorithms. Moreover, brain signals created by an external stimulus (e.g., vision) are most widely used in BCI-based applications, but it is impractical and infeasible in dynamic yet constrained environments. A question arises here: "How to circumvent constraints associated with stimulus-based signals? Is it feasible to apply non-invasive BCIS to read brain signals, and how to do so?". To a step further, I wonder could it be possible to accurately decode complete semantic-based command phrases in real time, and further achieve seamless and natural brain-robot systems for control and interactions? The project is for a team of 1-2 Master's students, and breakdown tasks will be assigned to each student later according to their skill set. What needs to be implemented and delivered at the end of the project? 1) A method package of brain signal pre-processing and feature formulation 2) An algorithm package of an encoder and a decoder of neural signals. 3) A model of training brain signals with spatial and temporal features. Last edited: 13/05/2025 | |
One project found.