Title: Closed-Loop Neurostimulators for Patient-Optimized Treatment of Interactable Epilepsy
Driven by its established therapeutic efficacy for movement disorders and psychiatric conditions, brain neuro-stimulation has been extensively investigated as a treatment option for patients with intractable epilepsy. The envisaged solution by the research community is an implantable device capable of long- term monitoring of neuronal activities with a high spatio-temporal resolution, processing the recorded data in real time, and triggering timely responsive neuro-stimulation upon detection of an upcoming seizure. The highly constrained energy budget of an implantable device, as well as the large variations across different patients’ brainwaves have introduced several implementation challenges and opportunities for biomedical circuits and systems (BioCAS) researchers.
For diagnosis, ASIC solutions with embedded deterministic or data-driven algorithms that employ a subset of dozens of different signal features (time and frequency domain) and classifiers (SVMs, DNNs, SNNs, etc.) have been reported, demonstrating a relatively high level of success in terms of detection accuracy, latency, and energy efficiency. Reported solutions also vary in terms of computing circuit implementation (digital, mixed-signal, analog, spike-based, etc.), training strategy (online vs offline) and target application (patient-specific vs. cross-patient). For treatment, optimization of the stimulation waveforms for each patient (i.e., a personalized therapy) has drawn much attention in recent years. It has introduced new challenges related to a lack of understanding of how stimulation parameters affect neuronal activities, the absence of a standard model of the brain that captures its complex electrochemical interactions, and simultaneous recording and stimulation.
In this tutorial, I will present the background, recent developments, and challenges in improving diagnosis accuracy, treatment efficacy, and energy efficiency of these implantable patient-optimized neurostimulators and will discuss possible future directions in this domain.
Hossein Kassiri received his Ph.D. degree in electrical and computer engineering from the University of Toronto in 2016. He joined the Electrical Engineering and Computer Science department at York University, Toronto, Canada, in July 2016, where he is currently an Associate Professor and the Director of the Integrated Circuits and Systems Laboratory and the Center for Microelectronics Prototyping and Test. In September 2015, he Co-Founded BrainCom Inc., which specialized in implantable brain-computer interfaces. His research interests include the area of design and development of wireless and battery-less multi-modal neural interfacing systems and their application in monitoring and treatment of neurological disorders. Dr. Kassiri’s publications have received a multitude of best paper awards including in IEEE BioCAS 2021 (best student paper), IEEE ISSCC 2017 (Jack Kilby Award), IEEE ISCAS 2016 (Best Biomedical Paper Award). He is also the recipient of the Ontario Brain Institute Entrepreneurship Award, Heffernan Commercialization Fellowship, and the CMC Brian L. Barge Award for Excellence in Microsystems. He was an Associate Editor of IEEE Transactions in Biomedical Circuits and Systems (2019-2021) and currently serves as the local arrangement chair for the IEEE BioCAS2023.
Title: Building Neuromorphic Devices to enhance Nervous System Interfaces in Bioelectronic Medicine
Bioelectronic medicine treats chronic diseases by sensing, processing, and modulating the electronic signals produced in the nervous system. While electronic circuits have been used for several years in this domain, the progress in microelectronic technology is now allowing increasingly accurate and targeted solutions for therapeutic benefits. To fully exploit this approach it is crucial to understand what aspects of the nerve signals are important, what is the effect of the stimulation, and what circuit designs can best achieve the desired result. Neuromorphic electronic circuits represent a promising design style for achieving this goal: their ultra-low power characteristics and biologically plausible time constants make them the ideal candidate for building optimal interfaces to real neural processing systems, enabling real-time closed-loop interactions with the biological tissue. In this tutorial, we highlight the main features of neuromorphic
circuits that are ideally suited for interfacing with the nervous system and show how they can be used to build closed-loop hybrid artificial and biological neural processing systems. We will explain which neural computational primitives can be implemented for carrying out computation on the signals sensed in these closed-loop systems and discuss the way to use their outputs for neural stimulation. We will conclude by showing examples of applications that follow this approach, highlight open challenges that need to be addressed, and propose actions required to overcome current limitations.
Dr. Elisa Donati (Member, IEEE) received the B.Sc., M.Sc. degrees in biomedical engineering from the University of Pisa, Pisa, Italy (cum laude), and the Ph.D. degree in biorobotics from the Sant’Anna School of Advanced Studies, Pisa, Italy, in 2016. She is currently a Senior Scientist with the Institute of Neuroinformatics, University of Zurich and ETHZ where she is training as a Neuromorphic Engineer. She is also Junior Group Leader at the Neuroscience Center Zurich in the Computational and Modeling and Motor System Group. Her research interests include how to interface neurorobotics and neuromorphic engineering for building smart and wearable biomedical devices. In particular, she is interested in designing VLSI systems for prosthetic devices, such as adaptive neuromorphic pacemakers. Another recent application includes a neuromorphic processor for controlling upper limb neuroprosthesis. She is investigating how to process EMG data to extract features to produce motor commands by using spiking neural networks. She is a TC member of Neural Systems and Applications of the circuit and system society and of the Biomedical circuit and system society.
Prof. Giacomo Indiveri is the director of the Institute of Neuroinformatics of the University of Zurich (UZH) and ETH Zurich and a dual Professor of Neuromorphic Cognitive Systems at UZH and ETH Zurich. He obtained an M.Sc. degree in electrical engineering in 1992 and a Ph.D. degree in computer science in 2004 from the University of Genoa, Italy. Engineer by training, Indiveri has also expertise in neuroscience, computer science, and machine learning. He has been combining these disciplines by studying natural and artificial intelligence in neural processing systems and in neuromorphic cognitive agents. His latest research interests lie in the study of spike-based learning mechanisms and recurrent networks of biologically plausible neurons, and in their integration in real-time closed-loop sensory-motor systems designed using analog/digital circuits and emerging memory technologies. His group uses these neuromorphic circuits to validate brain inspired computational paradigms in real-world scenarios, and to develop a new generation of fault-tolerant event-based neuromorphic computing technologies. Indiveri is senior member of the IEEE society, and a recipient of the 2021 IEEE Biomedical Circuits and Systems Best Paper Award. He is also an ERC fellow, recipient of three European Research Council grants.
Title: Ultra-low Power Telemetry Circuits for Body Area and IoT Applications
The ultra-low power (ULP) telemetry circuits play a significant role in the wireless body area applications as well as Internet-of-Things (IoT) with stringent energy constraints. In this talk, the major telemetry technologies for typical application scenarios will first be reviewed. The system constraints and requirements will be analyzed, and those popular telemetry circuits based on both electromagnetic (EM) transmission and non-EM approaches in recent literature will be discussed. After that, the talk will focus on the ULP narrow-band telemetry circuits. The design principles of ULP short-range telemetry circuits will be illustrated by going deep into the technical details of two design examples recently published, including a 915-MHz sub-sampling phase-tracking receiver, in which the subsampling technique is proposed to reduce the power consumption of local oscillator (LO), and a 400MHz/900MHz dual-band combo transmitter which adopts the multi-phase digital power amplifier (DPA) based QAM modulation and edge combiner techniques. Both the system-level circuit innovations and the transistor-level design techniques for the purpose of ultra-low power consumption will be discussed.
Hanjun Jiang received the B.S. degree in electronic engineering from Tsinghua
University, Beijing, China, in 2001, and the Ph.D. degree in electrical engineering from Iowa State University, Ames, IA, USA, in 2005. From 2005 to 2006, he was with Texas Instruments, Dallas, USA. He has been with the School of Integrated Circuits (formerly the Institute of Microelectronics), Tsinghua University since 2007, where he is currently an associate professor and the vice dean. His current research interest mainly focuses in the area of energy-efficient circuits and systems design, including the signal acquisition circuit, short-range transceiver and system-level integration, with an emphasis on the medical and healthcare applications. He has authored and co-authored over 1340 peer-reviewed journal and conference papers, and contributed to 3 books. He holds more than 30 patents. He was the IEEE Solid-State Circuits Society Beijing Chapter Chair from 2015 to 2018. He is now an associate editor of the IEEE Transactions on Biomedical Circuits and System (TBioCAS), IEEE Transactions on Circuits and Systems II: Express Briefs (TCAS-II) and Microelectronics (in Chinese).
Title: On the Design and Application of CMOS Micro Electrode Arrays for Neural Tissue Recording and Stimulation with high Spatiotemporal Resolution
Ex-vivo Micro Electrode Arrays (MEAs) have become a technical standard tool to record
nerve signals from neural tissue and to stimulate the tissue. Whereas MEA technology
had begun with passive devices decades ago – i.e. with chips having no active
electronic devices on board and with such devices still being operated by a broad
application community today -, aiming for far increased spatiotemporal resolution within
the last two decades active CMOS-based MEAs have been suggested in the literature.
Part of these approaches meanwhile also have been commercialized.
In this tutorial, we will consider basic non-invasive, i.e. extracellular recording and
stimulation techniques, highlight related design philosophies and challenges of high
density CMOS MEAs, consider CMOS integration issues with respect to interfacing
materials, and discuss circuit and system design aspects with respect to signal-to-noise
Moreover, a short outlook will be given concerning the transition from in-vitro to in-vivo
applications and data processing and handling issues in case of real high density
Roland Thewes received the PhD degree in Electrical Engineering from University of
Dortmund, Germany, in 1995. In 1994, he joined the Research Labs of Siemens AG,
where he was active in the design of non-volatile memories and in the field of reliability
and yield of analog CMOS circuits. From 1997-1999, he managed projects concerning
design for manufacturability, reliability, analog device performance, and analog CMOS
circuit design. From 2000-2005, he was responsible for the Lab on Mixed-Signal
Circuits of Corporate Research of Infineon Technologies focusing on CMOS-based bio-
sensors, low voltage analog CMOS circuit design, and device-circuit interaction. From
2006-March 2009, he was heading a department focusing on Advanced DRAM Core
Circuitry in the Product Development Division of Qimonda. Since April 2009, he is a full
professor at TU Berlin focusing on CMOS-based sensors and actuators with emphasis
on bio-sensing and neural interfacing purposes. He has authored or co-authored more
than 180 technical publications and a similar number of granted patents and patent
applications. He has been serving on various Scientific Technical Program and
Executive Committees, among these as IEEE BioCAS 2021 General Chair.