Thesis Project Form
Title (tentative): Real time detection of Local Field Potentials for closed-loop intracortical microstimulation| Thesis advisor(s): Chiappalone Michela, Simone Del Corso | E-mail: |
| Address: Via Opera Pia 13, 16145 Genova | Phone: |
Description
Motivation and application domain
Neurostimulation based on Local Field Potentials (LFPs) holds great potential for improving motor recovery after brain injuries, but real-time closed loop stimulation remains a challenge due to the need for high speed signal processing and adaptive algorithms. This project aims to overcome these limitations by developing an FPGA based system capable of dynamically adjusting stimulation in response to neural activity, possibly exploiting a Model Based Design approach.
General objectives and main activities
The main objective of this thesis is the implementation and validation of a pre-existing algorithm detecting LFP in real time. Neurostimulation based on the LFP detection will, in our hypothesis, enhance motor recovery in rats with brain injuries. The algorithm will be either based on Simulink Real-Time or translated into HDL language and implemented on an FPGA using a Model-Based Design (MBD) approach, depending on the available possibilities. The aim is to create a real-time system capable of adapting stimulation based on neural signals, improving the effectiveness of neuro-motor rehabilitation.
Main Activities:
1. Implementation of the algorithm in MATLAB/Simulink and possibly also Python.
2. Evaluating the algorithm's accuracy and reliability on simulated datasets.
3. Conversion of the algorithm into HDL language using Model-Based Design.
4. Simulation and validation of the algorithm in an FPGA environment.
5. Comparison of the algorithm’s performance in simulated and in vivo environments.
Main Activities:
1. Implementation of the algorithm in MATLAB/Simulink and possibly also Python.
2. Evaluating the algorithm's accuracy and reliability on simulated datasets.
3. Conversion of the algorithm into HDL language using Model-Based Design.
4. Simulation and validation of the algorithm in an FPGA environment.
5. Comparison of the algorithm’s performance in simulated and in vivo environments.
Training Objectives (technical/analytical tools, experimental methodologies)
• Neurophysiological signal processing
• Real Time implementation
• Data analysis and statistical testing
• Experiments in vivo: in vivo set-up use and optimization
• Real Time implementation
• Data analysis and statistical testing
• Experiments in vivo: in vivo set-up use and optimization
Place(s) where the thesis work will be carried out: DIBRIS Department and LiSTechLab (joint Lab UNIGE-San Martino) at San Martino Hospital- Ist Nord.
Additional information
Pre-requisite abilities/skills: Linear algebra, Signals and Systems, Programming in MATLAB or Python, Statistical methods, Biological systems
Maximum number of students: 1