Medical Devices Speaker Series 2023: Develop Advanced Brain-Machine Interfaces with Biomedical Signal Processing and AI Tools from MathWorks

Biomedical signal processing is at the heart of Brain-Machine Interfaces (BMIs), offering immense potential to enhance the quality of life for individuals with neurological disorders and disabilities. This presentation by Vikash Gilja from UC San Diego explores how these advanced systems necessitate the integration of sophisticated biomedical signal processing techniques and AI algorithms.

Discover how MathWorks solutions can help you:

• Acquire and analyze neural signals: Utilize powerful tools for biomedical signal processing and filtering to extract meaningful information from brain activity.
• Develop and implement AI algorithms: Leverage machine learning and deep learning techniques to decode neural signals and translate them into control commands.
• Design and simulate BMI systems: Model and test your BMI in a virtual environment before implementation, ensuring safety and efficacy.
• Generate production-ready code: Automatically generate code from your algorithms, streamlining the development process and reducing the potential for errors.

This presentation was given at the 2023 MathWorks Medical Devices Speaker Series, which is a forum for researchers and industry practitioners using MATLAB^® and Simulink^® to showcase and discuss their state-of-the-art innovations in the areas of medical device research, prototyping, and compliance with FDA/MDR regulations for device certification.

About the presenter:

Vikash Gilja is an associate professor in the Electrical and Computer Engineering Department and is a member of the Neuroscience Graduate Program faculty at the University of California, San Diego (UCSD). He directs the Translational Neural Engineering Laboratory, which develops and expands the capabilities of neural prosthetic systems. To realize this goal, the lab develops machine learning–based algorithms that interpret multimodal physiology data in real time, and it validates these algorithms by conducting experiments in multiple clinical settings and animal models. Gilja received a B.S. degree in brain and cognitive sciences and B.S./M.Eng. degrees in electrical engineering and computer science from MIT in 2003 and 2004 and his Ph.D. in computer science at Stanford University in 2010.