IEEE Brain Webinar Series

IEEE Brain Webinar Series

Learn from the top subject matter experts in brain research and neurotechnology. The objective of the IEEE Brain Webinar Series is to be a point of learning for engineering and technology advancements that improve our understanding of the brain to treat diseases and to improve human condition.  We will be hosting a technical webinar approximately once every 2 months.

2021 Webinars

Tuesday, 23 FebruaryGuosong HongUC StanfordWatch the recorded webinar
Thursday,18 MarchDamien CoyleUlster UniversityWatch the recorded webinar
Thursday, 20 MayVince CalhounGeorgia State UniversityWatch the recorded webinar
Tuesday, 15 June Julie GrollierCNRS - ThalesWatch the recorded webinar
Friday, 09 JulyPeter BandettiniNIHWatch the recorded webinar
Friday, 13 AugustTodd ColemanStanford UniversityWatch the recorded webinar
Thursday, 16 SeptemberJosé del R. MillánUniversity of Texas, AustinWatch the recorded webinar
Thursday, 28 OctoberRylie GreenImperial College LondonView for free until 28 November
NovemberMilin ZhangTsinghua UniversityTBD
DecemberJeffrey HerronUniversity of WashingtonTBD

Upcoming Webinars

Check back soon for more upcoming webinars

Past Webinars

Improving Communication With the Brain Through Electrode Technologies

Dr. Rylie Green, Professor, Department of Bioengineering, Imperial College London
Improving Communication With the Brain Through Electrode Technologies  (In partnership with IEEE Engineering in Medicine & Biology Society)
Thursday, 28 October 2021
Over the past 30 years bionic devices such as cochlear implants and pacemakers, have used a small number of metal electrodes to restore function and monitor activity in patients following disease or injury of excitable tissues. Growing interest in neurotechnologies, facilitated by ventures such as BrainGate, Neuralink and the European Human Brain Project, has increased public awareness of electrotherapeutics and led to both new applications for bioelectronics and a growing demand for less invasive devices with improved performance. Coupled with the rapid miniaturisation of electronic chips, bionic devices are now being developed to diagnose and treat a wide variety of neural and muscular disorders. Of particular interest is the area of high resolution devices that require smaller, more densely packed electrodes. Due to poor integration and communication with body tissue, conventional metallic electrodes cannot meet these size and spatial requirements.
We have developed a range of polymer based electronic materials including conductive hydrogels (CHs), conductive elastomers (CEs) and living electrodes (LEs). These technologies provide synergy between low impedance charge transfer, reduced stiffness and an ability to be provide a biologically active interface. A range of electrode approaches are presented spanning wearables, implantables and drug delivery devices. This talk outlines the materials development and characterisation of both in vitro properties and translational in vivo performance. The challenges for translation and commercial uptake of novel technologies will also be discussed.

View for free until 28 November
Brain-Machine Interfaces: Beyond Decoding

Dr. Jose del R. Millan, Carol Cockrell Curran Endowed Chair Professor, Dept. of Electrical and Computer Engineering & Dept. of Neurology, The University of Texas at Austin
Brain-Machine Interfaces: Beyond Decoding  (In partnership with IEEE Systems, Man, and Cybernetics Society)
Thursday, 16 September 2021
A brain-machine interface (BMI) is a system that enables users to interact with computers and robots through the voluntary modulation of their brain activity. Such a BMI is particularly relevant as an aid for patients with severe neuromuscular disabilities, although it also opens up new possibilities in human-machine interaction for able-bodied people. Real-time signal processing and decoding of brain signals are certainly at the heart of a BMI. Yet, this does not suffice for subjects to operate a brain-controlled device. In the first part of my talk I will review some of our recent studies, most involving participants with severe motor disabilities, that illustrate additional principles of a reliable BMI that enable users to operate different devices. In particular, I will show how an exclusive focus on machine learning is not necessarily the solution as it may not promote subject learning. This highlights the need for a comprehensive mutual learning methodology that foster learning at the three critical levels of the machine, subject and application. To further illustrate that BMI is more than just decoding, I will discuss how to enhance subject learning and BMI performance through appropriate feedback modalities. Finally, I will show how these principles translate to motor rehabilitation, where in a controlled trial chronic stroke patients achieved a significant functional recovery after the intervention, which was retained 6-12 months after the end of therapy.

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Electrophysiologic Monitoring and Modulation of Enteric Nervous Systems

Dr. Todd P. Coleman, Associate Professor, Stanford University
Electrophysiologic Monitoring and Modulation of Enteric Nervous Systems
Friday, 13 August 2021
We will highlight recent technological and methodological advances in deploying miniaturized technologies that can monitor the spatial electrophysiologic patterns of the visceral nervous system. As an example, we will discuss recent developments of thin, stretchable, wireless biosensor patches that can be embedded within routinely used medical adhesives for recording electrophysiologic patterns of the GI tract. We will also showcase recent developments in array signal processing that enable non-invasive tracking, and source localization, of the slow wave patterns associated with the GI tract. We will illustrate how such systems can also be used in tandem with novel miniaturized pacing devices to can enable closed-loop neuromodulation of the enteric nervous system. We will conclude with a summary of the knowns and unknowns in how multi-organ physiology research, technology miniaturization, and data science may create unique opportunities for the intersection of electrical engineering and neuroscience.

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The Challenge and Opportunities of Mapping Cortical Layer Activity and Connectivity with fMRI

Dr. Peter Bandettini, Chief, Section on Functional Imaging Methods; Director, Functional Magnetic Resonance Imaging Core Facility (FMRIF), NIMH
The Challenge and Opportunities of Mapping Cortical Layer Activity and Connectivity with fMRI
Friday, 09 July 2021
A major outstanding challenge in neuroscience is to integrate across levels of investigation, linking genes, molecules, cells, microcircuits, regions, systems and behavior. This will require bringing together evidence from sources across different spatial scales—from the microscopic, such as electrophysiological recordings in animals, to the macroscopic, such as conventional neuroimaging in humans. The mesoscale technique of depth-dependent fMRI, or “layer fMRI” which can be applied non-invasively in awake, behaving humans, is a critical missing link to bridge this gap. Specifically, layer fMRI has the potential to open up human neuroimaging research as functional information at the layer-level allows not only for inferences to be made about location of activation but also for inferences to be made on feedforward and feedback activity from each region, based on our knowledge of the organization of cortical layers. These inferences may therefore better inform network models, provide insights into the functional causality of specific regions, and to provide informative maps of cortical hierarchy.

In this talk I outline the technical challenges and current solutions to layer fMRI. Specifically, I describe our acquisition strategies for maximizing resolution, spatial coverage, time efficiency as well as, perhaps most importantly, vascular specificity. Novel applications from our group, including mapping feedforward and feedback connections to M1 during task and sensory input modulation and S1 during a sensory prediction task are be shown. Layer specific activity in dorsal lateral prefrontal cortex during a working memory task is also demonstrated. Additionally, I’ll show preliminary work on mapping whole brain layer-specific resting state connectivity and hierarchy.

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Dynamical Neuromorphic Systems with Julie Grollier

Dr. Julie Grollier, CNRS/Thales lab, Palaiseau, France
Dynamical Neuromorphic Systems  (In partnership with IEEE Magnetics Society)
Tuesday, 15 June 2021
In this talk, I aim to show that the dynamical properties of emerging nanodevices can accelerate the development of smart, and environmentally friendly chips that inherently learn through their physics.

The goal of neuromorphic computing is to draw inspiration from the architecture of the brain to build low-power circuits for artificial intelligence. I will first give a brief overview of the state of the art of neuromorphic computing, highlighting the opportunities offered by emerging nanodevices in this field, and the associated challenges. I will then show that the intrinsic dynamical properties of these nanodevices can be exploited at the device and algorithmic level to assemble systems that infer and learn though their physics. I will illustrate these possibilities with examples from our work on spintronic neural networks that communicate and compute through their microwave oscillations, and on an algorithm called Equilibrium Propagation that minimizes both the error and energy of a dynamical system.

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From 1D to 5D: Data-driven Discovery of Whole-brain Dynamic Connectivity in fMRI Data

Dr. Vince Calhoun, Founding Director, Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA
From 1D to 5D: Data-driven Discovery of Whole-brain Dynamic Connectivity in fMRI Data  (In partnership with IEEE Signal Processing Society)
Thursday, 20 May 2021
The analysis of functional magnetic resonance imaging (fMRI) data can greatly benefit from flexible analytic approaches. In particular, the advent of data-driven approaches to identify whole-brain time-varying connectivity and activity has revealed a number of interesting relevant variation in the data which, when ignored, can provide misleading information. In this lecture I will provide a comparative introduction of a range of data-driven approaches to estimating time-varying connectivity. I will also present detailed examples where studies of both brain health and disorder have been advanced by approaches designed to capture and estimate time-varying information in resting fMRI data. I will review several exemplar data sets analyzed in different ways to demonstrate the complementarity as well as trade-offs of various modeling approaches to answer questions about brain function. Finally, I will review and provide examples of strategies for validating time-varying connectivity including simulations, multimodal imaging, and comparative prediction within clinical populations, among others. As part of the interactive aspect I will provide a hands-on guide to the dynamic functional network connectivity toolbox within the GIFT software, including an online didactic analytic decision tree to introduce the various concepts and decisions that need to be made when using such tools

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Motor Imagery BCI for Cognitive Profiling in Disorders of Consciousness and Prospects for Direct Speech BCI with Imagined-speech

Prof. Damien Coyle, Intelligent Systems Research Centre, Ulster University
Motor Imagery BCI for Cognitive Profiling in Disorders of Consciousness and Prospects for Direct Speech BCI with Imagined-speech  (In partnership with IEEE Computational Intelligence Society)
Thursday, 18 March 2021

This webinar will cover two current hot topics in EEG-based brain-computer interface research and research ongoing at the Intelligent Systems Research Centre.

Part 1 will focus on assessment of patients with prolonged disorder of consciousness (PDoC). Motor imagery brain-computer interface (MI-BCI) may facilitate willful modulation of sensorimotor oscillations in patients with PDoC, enabling assessment of awareness and question answering by imagining movement and thus, potentially, movement-independent neuropsychological assessment. We evaluated this potential with a cohort of PDoC patients (n=24). Whilst results revealed patients across the PDoC spectrum have capacity to learn to modulate sensorimotor rhythms and respond to closed questions, differences in patient cognition are more likely to be revealed after extended training with feedback and more intensive question-and-answer sessions.

Part 2 will focus on direct speech BCIs. Several recent studies have harnessed overt speech to examine linguistic communication through neural signals. While imagined speech is the holy grail modality for a BCI based on language, systematic study of imagined speech has been relatively sparse. The phenomenology of imagined speech, its relationship to overt speech, and the effect of different stimuli on efforts to elicit and label its neural correlations to enable algorithms to learn to detect and classify it, are currently not well understood. Employing the first picture-naming paradigm in speech BCI research, this talk will show that effects of stimuli/cues on speech decoding from EEG are highly significant, whilst linguistic properties of semantics and syntax are not and that overt speech is easier to decode from EEG than imagined speech.

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Seeing the Sound: Optical Neural Interfaces for In Vivo Neuromodulation

Dr. Guosong Hong, Assistant Professor, Stanford, Materials Science and Engineering and Neurosciences Institute
Seeing the Sound: Optical Neural Interfaces for In Vivo Neuromodulation
Tuesday, 23 February 2021

Optogenetics has transformed experimental neuroscience by manipulating the activity of specific cell types with light, enabling in vivo neuromodulation with millisecond temporal resolution. Visible light with wavelengths between 430 nm and 640 nm is used for optogenetics, limiting penetration depth in vivo and resulting in an invasive fiber-tethered interface that damages the endogenous neural tissue and constrains the animal’s free behavior. In this talk, I will present two recent methods to address this challenge: “sono-optogenetics” and “macromolecular infrared nanotransducers for deep-brain stimulation (MINDS)”. In the first method, we demonstrate that mechanoluminescent nanoparticles can act as circulation-delivered nanotransducers to convert sound into light for noninvasive optogenetic neuromodulation in live mice. In the second method, we demonstrate 1064-nm near-infrared-II light can penetrate the brain to reach 5-mm depths for modulating neural activity in tether-free, freely behaving animals. I will present an outlook on how new optical neural interfaces may advance neuroscience research by reducing the invasiveness and mechanical restraints in live animals and even humans.

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Brain Machine Interfaces: Concept to Clinic
Dr. Vikash Gilja, Associate Professor, Department of Electrical & Computer Engineering and Neuroscience Graduate Program, University of California, San Diego (UCSD)
Brain Machine Interfaces: Concept to Clinic
Wednesday, 28 October 2020

Over the last two decades neural prostheses that aim to restore lost motor function have moved quickly from concept to laboratory development and clinical demonstration. In parallel, advances in neural interfacing technologies poised to broaden clinical application of these prostheses are actively in development in both academic and industry settings. In this talk, I will provide a broad overview of the technical history of these neural prostheses starting from enabling neurophysiology insights to work currently being conducted. Additionally, I will describe research within my own lab with the goal of augmenting neural prosthesis performance and expanding their potential application space. This work will highlight key enabling research collaborations in multiple clinical settings and the development of complementary animal models that accelerate development. We will take a few deep dives to describe the application of statistical signal processing, machine learning, and algorithm design to this research domain.

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Optimizing Control and Learning in Neural Interfaces
Dr. Amy Orsborn, Clare Boothe Luce Assistant Professor in Electrical & Computer Engineering and Bioengineering, University of Washington
Optimizing Control and Learning in Neural Interfaces
Tuesday, 30 June 2020
Direct interfaces with the brain provide exciting new ways to restore and repair neurological function. For instance, motor Brain-Machine Interfaces (BMIs) can bypass a paralyzed person's injury by repurposing intact portions of their brain to control movements. Recent work shows that BMIs do not simply "decode" subjects' intentions - they create new systems subjects learn to control. To improve BMI performance and usability, we must therefore understand how to optimize learning and control in these systems. I will present a survey of recent work and new directions exploring how the design of BMI systems influence BMI performance. I'll touch on the importance of control loop design, brain-decoder interactions and multi-learner approaches, and network-informed neural signal selection. These examples highlight the role of learning and closed-loop in BMIs, and demonstrate the promise of engineering approaches based on optimizing learning and control along with information "decoding."

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A Large-scale Standardized Physiological Pipeline Reveals Functional Organization of the Mouse Visual Cortex
Dr. Saskia de Vries, Assistant Investigator, Allen Institute for Brain Science
A Large-scale Standardized Physiological Pipeline Reveals Functional Organization of the Mouse Visual Cortex
Tuesday, 31 March 2020

An important open question in visual neuroscience is how visual information is represented in cortex. Important results characterized neural coding by assessing the responses to artificial stimuli, with the assumption that responses to gratings, for example, capture the key features of neural responses, and deviations, such as extra-classical effects, are relatively minor. The failure of these responses to have strong predictive power has renewed these questions. It has been suggested that this characterization of visual responses has been strongly influenced by the biases inherent in recording methods and the limited stimuli used in experiments. In creating the Allen Brain Observatory, we sought to reduce these biases by recording large populations of neurons in the mouse visual cortex using a broad array of stimuli, both artificial and natural. This open dataset is a large-scale, systematic survey of physiological activity in the awake mouse cortex recorded using 2-photon calcium imaging. Neural activity was recorded in cortical neurons of awake mice who were presented a variety of visual stimuli, including gratings, noise, natural images, and natural movies. This dataset consists of over 63,000 neurons recorded in over 1300 imaging sessions, surveying 6 cortical areas, 4 cortical layers, and 14 transgenically defined cell types (Cre lines).

We found that visual responses throughout the mouse cortex are highly variable. Using the joint reliabilities of responses to multiple stimuli, we classify neurons into functional classes and validate this classification with models of visual responses. Only 10% of neurons in the mouse visual cortex show reliable responses to all of the stimuli used, and are reasonably well predicted by linear-nonlinear models. The remaining neurons fall into classes characterized by responses to specific subsets of the stimuli and the neurons in the largest class do not reliably responsive to any of the stimuli. These classes reveal a functional organization within the mouse visual cortex wherein putative dorsal areas show specialization for visual motion signals.

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Multimodal Imaging in Understanding Brain Diseases
Dr. Ruiqing Ni,
Junior Group Leader, Institute for Biomedical Engineering, ETH Zurich & University of Zurich
Multimodal Imaging in Understanding Brain Diseases
Tuesday, 29 October 2019

The advances in neuroimaging in the last decades have bridged the translational gap, and enabled our understanding of brain under physiological and disease conditions. Multiscale and multimodal imaging such as positron emission tomography, magnetic resonance imaging, optoacoustic and fluorescence imaging have provide molecular, structural, and functional insights at cellular, circuit and whole brain levels. The use of maging biomarkers has also assisted the early and accurate diagnosis of brain disorders, and facilitated personalized medicine. This webinar will focus on the development of novel brain imaging techniques, as well as their application in the field of Alzheimer’s disease. Multimodal high-resolution imaging tools were developed for non-invasive visualization of the neuropathology (amyloid-beta and tauopathy), brain connectivity, and atrophy in mouse models of Alzheimer’s disease.

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Modeling the Representation of Object Boundary Contours in Human fMRI Data
Dr. Mark Lescroart,
Assistant Professor, Cognitive & Brain Sciences Group, Department of Psychology, University of Nevada, Reno

Modeling the Representation of Object Boundary Contours in Human fMRI Data
Tuesday, 13 August 2019

The human visual system consists of a hierarchy of areas, each of which represents different features of the visual world. Recent studies have revealed that most brain areas—and even many individual neurons—represent information about multiple visual features. Thus, a complete model of the brain must specify the relative importance of multiple visual features across the visual hierarchy. This talk will describe our work to estimate the importance of object boundary contours relative to other features.
Boundary contours define the edges of figural objects in scenes, and figure/ground segmentation has long been held to be a critical process in human vision. However, the relative importance of boundary contours compared to both lower- and higher-level features (e.g. motion energy and visual categories) remains unknown. To address this issue, we measured fMRI responses while human subjects viewed two sets of movies that varied in many feature dimensions: rendered movies of artificial scenes and cinematic movies. We modeled responses to both sets of movies independently using the same three models: models of motion energy, object boundary contours, and visual categories. We used the encoding models to predict withheld fMRI data, and used variance partitioning to determine whether the various models explained unique or shared variance in each dataset. We found that the pattern of unique variance explained by the three models was qualitatively consistent across both datasets, with unique variance explained by boundary contours in Lateral Occipital cortex and other areas. However, the three models also shared substantially more variance in the cinematic movies, likely due to correlations between model features. For example, much of the motion energy in the cinematic movies was a result of people moving. The shared variance between all three models in the cinematic movies in particular highlights the need for complex stimulus sets in which features in different models are de-correlated from each other.

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Neurophotonic Systems: From Flexible Polymer Implants to in situ Ultrasonically-driven Light Guides with Dr. Maysam Chamanzar
Dr. Maysam Chamanzar,
Assistant Professor of Electrical Computer Engineering,
Carnegie Mellon University

Neurophotonic Systems: From Flexible Polymer Implants to in situ Ultrasonically-driven Light Guides
Tuesday, 18 June 2019

Understanding the neural basis of brain function and dysfunction may inform the design of effective therapeutic interventions for brain disorders and mental illnesses. Optical techniques have been recently developed for structural and functional imaging as well as targeted stimulation of neural circuits. One of the challenges of optical modality is light delivery deep into the brain tissue in a non-invasive or at least minimally invasive way.

Scattering and absorption prevents deep penetration of light in tissue and limits light-based methods to superficial layers of the tissue. To overcome this challenge, implantable photonic waveguides such as optical fibers or graded-index (GRIN) lenses have been used to deliver light into the tissue or collect photons for imaging. Existing large and rigid optical waveguides cause damage to the brain tissue and vasculature. In this talk, Dr. Maysam Chamanzar will discuss his research on developing next generation optical neural interfaces. First, Dr. Chamanzar will introduce a novel compact flexible photonic platform based on biocompatible polymers, Parylene C and PDMS, and GaN active light sources for optogenetic stimulation of neural circuits with high spatiotemporal resolution. This photonic platform can be monolithically integrated with implantable neural probes.

Then, Dr. Chamanzar will discuss his recent work on developing a novel complementary approach to guide and steer light in the brain using non-invasive ultrasound. Dr. Chamanzar will show that ultrasound waves can sculpt virtual graded-index (GRIN) waveguides in the tissue to define and steer the trajectory of light without physically implanting optical waveguides in the brain.

These novel neurophotonic techniques enable high-throughput bi-directional interfacing with the brain to understand the neural basis of brain function and design next generation neural prostheses.

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Euisik Yoon, Ph.D.
Professor, Dept. of Electrical Engineering and Computer Science
Professor, Dept. of Biomedical Engineering
Director, NSF International Program for Advancement of Neurotechnology
University of Michigan
Fiberless Optoelectrodes for Selective Optical Neuromodulation at Cellular Resolution
Tuesday, 30 April 2019

This talk will review the evolution of Michigan neural probe technologies toward scaling up the number of recording sites, enhancing the recording reliability, and introducing multi-modalities in neural interface including optogenetics. Modular system integration and compact 3D packaging approaches have been explored to realize high-density neural probe arrays for recording of more than 1,000 channels simultaneously. In order to obtain optical stimulation capability, optical waveguides were monolithically integrated on the silicon substrate to bring light to the probe shank tips. Excitation and inhibition of neural activities could be successfully validated by switching the wavelengths delivered to the distal end of the waveguide. For scaling of the number of stimulation sites, multiple micro-LEDs were directly integrated on the probe shank to achieve high spatial temporal modulation of neural circuits. Independent control of distinct cells was demonstrated ~50 μm apart and of differential somato-dendritic compartments of single neurons in the CA1 pyramidal layer of anesthetized and freely-moving mice.

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Anton Arkhipov, Ph.D.
Associate Investigator
Allen Institute for Brain Science
Data-Driven Modeling of Brain Circuits Based on a Systematic Experimental Platform
Wednesday, 20 February 2019

The Mindscope project at the Allen Institute aims to elucidate mechanisms underlying cortical function in the mouse, focusing on the visual system. This involves concerted efforts of multiple teams characterizing cell types, connectivity, and neuronal activity in behaving animals. An integral part of these efforts is the construction of models of the cortical tissue and cortical computations. To achieve this, multi-model experimental data are integrated into a highly realistic 230,000-neuron model of the mouse cortical area V1. We perform systematic comparisons of simulated responses to in vivo experiments and investigate the structure-function relationships in the models to make mechanistic predictions for experimental testing. To enable this work, we developed the software suite called Brain Modeling ToolKit (BMTK) and a modeling file format called SONATA. These tools, the models, and simulation results are all being made freely available to the community via the Allen Institute Modeling Portal.

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