Title: Brain Machine Interface: Challenges and Opportunities Date/Time: (PST)- 12:00pm to 1:00pm Thu, Oct 23 2025 Abstract: Brain Machine interfaces have the potential to revolutionize therapy for neurological diseases, because they target the nervous system with high spatiotemporal resolution as opposed to alternative therapies. Next-generation brain machine interfaces will benefit from an implantable neural recording IC with a dense, high channel count recording array that can be directly matched to a micro-electrode array (MEA) at the pitch of neurons (≈30 µm) to effectively capture spatiotemporal patterns of neural activity at single-cell resolution. These devices must support simultaneous recording from multiple thousands of neurons within the form factor and power budget of a fully implanted device. Hence, there is a requirement for an architectural paradigm shift to meet the design targets. In this talk, we will delve into specific challenges and approaches to achieve intended targets. Speaker Bio: Dante G. Muratore received a B.Sc. and an M.Sc. degree in Electrical Engineering from Politecnico of Turin, Italy in 2012 and 2013, respectively. He received a Ph.D. degree in Microelectronics from the University of Pavia, Italy in 2017 in the Integrated Microsystems Lab. From 2015 to 2016, he was a Visiting Scholar at Microsystems Technology labs at the Massachusetts Institute of Technology, USA. From 2016 to 2020, he was a Postdoctoral Fellow at Stanford University, USA. He is the recipient of the Wu Tsai Neurosciences Institute Interdisciplinary Scholar Award. Since 2020, he is an assistant professor in the Bioelectronics Section at Delft University of Technology, Netherlands, where he leads the Smart Brain Interfaces group. His research focuses on hardware design for brain-machine interfaces, bioelectronics and machine learning. https://microelectronics.tudelft.nl/People/bio.php?id=690 (https://ieeemeetings.webex.com/ieeemeetings/j.php?MTID=m3d5beb80b9d84d4efbce27fdda8cc485) https://ieeemeetings.webex.com/ieeemeetings/j.php?MTID=m3d5beb80b9d84d4efbce27fdda8cc485 Meeting number: 2535 991 4718 Join from a video system or application Dial [email protected] You can also dial 173.243.2.68 and enter your meeting number. To dial from an IEEE Video Conference System: *1 2535 991 4718 Tap to join from a mobile device (attendees only) (tel:%2B1-415-655-0002,,*01*25359914718%23%23*01*) United States Toll (tel:1-855-282-6330,,*01*25359914718%23%23*01*) United States Toll Free Virtual: https://events.vtools.ieee.org/m/490922

We are in the midst of an artificial intelligence (AI) revolution. It may seem that AI development is recent, but the term AI was coined (1955) not too long after the invention of computing devices. However, the idea that a machine can behave like a human being is even older. The term “automaton” was used for it. Please see: - (https://www.tableau.com/data-insights/ai/history) - (https://en.wikipedia.org/wiki/Logic_Theorist) What has changed, though, in recent times, is the amount of computation that can be performed. GPUs have made tremendous progress, and it is now possible to do heavy computation with very large amount of memory in real time. The terms AI and machine learning (ML) are normally used interchangeably though there are differences. We will discuss what is the difference between them. While AI tries to mimic the human behavior, ML is a set of statistical tools to look at the data to find patterns without direct instructions. In some ways, it is a subset of AI. We will dig a little deeper into how AI works. This will give us a better understanding of what kind of problems it can solve effectively and where it should be avoided, or one needs enhanced or use better tools. Neural networks are normally used to perform AI calculations: Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) are some examples. They have their origin in the way the human brain works. We will discuss the biological basis of neural networks and discuss how these networks are implemented. We will focus on concepts and will develop intuition about how they are trained. We will also see how and when a certain type of network can be used. We will go over some caution around data analysis and what to watch out for when using AI. Speaker(s): Dr Md Usman Agenda: - 14:00 to 14:10 PM : Welcome to IEEE OC EMBS and general introduction - 14:10 to 14:20 PM : Introduction by Gora Datta, FHL7 - 14:20 to 15:50 PM: Expert Lecture by Dr Md Usman - The difference between AI and ML, - Biological basis of some AI networks, - Basics of how an AI model is trained and what does it mean, - Advantages and disadvantages of using AI – why should we use it, and what does it measure anyways? - What are large language models (LLM), and their usefulness in medicine, - Will understand some examples of using AI, and - Know when to use AI for health measurements. - 15:55 to 16:00 PM : Wrap Up Room: Emerald Cove, Bldg: Bealle Applied Innovation, 5270 California Ave, Gora Datta, Irvine, California, United States, 92617, Virtual: https://events.vtools.ieee.org/m/507676