A Brain-Computer Interface Framework for fNIRS-based Neurofeedback Therapy

Gain insights into the brain and optimize its function! This project aims to develop a software framework for a brain-computer interface utilizing functional near-infrared spectroscopy (fNIRS). The framework will be employed in a neurofeedback study with a focus on modulating motor brain regions.

Neurological disorders, such as stroke, attention deficit hyperactivity disorder (ADHD), and major depressive disorder, are major causes of disability worldwide. However, the current therapeutic approaches often prove ineffective or result in severe side effects, highlighting the need for alternative methods. One of the most promising and side-effect-free approaches is the use of brain-computer interfaces (BCIs). BCIs employ a brain imaging system to measure brain activity, which is then transmitted to a robot or computer screen, providing feedback to the user. Neurofeedback, a form of BCI therapy, involves displaying the patient's brain activity on a screen and allowing them to modulate it in real-time, directly targeting the source of the disorder : the brain itself.

This project aims to utilize our custom-built fNIRS system to conduct a neurofeedback study. The primary objective is to implement Python code for real-time analysis and visualization of fNIRS data. A study involving 10 healthy subjects will be performed, and the resulting data will be analyzed using common statistical methods.

Tasks :

• Conduct a comprehensive literature review and define the study protocol.
• Implement and explore algorithms for processing fNIRS data.
• Visualize fNIRS data effectively.
• Perform a neurofeedback study involving 10 healthy subjects.
• Analyze the collected fNIRS measurements.