ISSCC 2014 Short Course Transcription System Architectures and Strategies for Bi-Directional Circuit Interfacing for

Bioelectronic Systems Instructor: Timothy Denison

1. Acknowledgements First! Thank you, Willie. I’ll go ahead and get started. Just some quick acknowledgements to Firat, Pedram Mohseni at Case Western, supplied me a number of slides. I pull on some retinal prosthetics work from Albrecht Rothermel, and Anantha and Naveen have a little bit of digital processing as well.

2. Disclosures/Conflicts The other thing is I’m an industry member, and Medtronic is regulated, so I want to make it clear that pretty much all the circuits shown in this talk should be considered research, investigational use only, and not approved for any commercial sale in the United States.

3. Electrical Signals So we’re going to do a little bit of tack: we’ve heard a lot about electrode interfaces in sensing and how we can apply those. I’m going to shift over to start talking about sending information into the nervous system – so information and energy, particularly with an interest for therapeutic effect. So this is an old idea back in Roman times; they would actually treat gout and sometimes chronic migraine by attaching themselves to a live torpedo fish, and then using the electrical modulation – electrical shocks – in order to get some therapeutic benefits.

4. Neural Interfaces (2014) We’ve moved quite a ways, of course, into 2014. This is an example of a deep brain stimulator today, but it still has many of the core elements. We have electrodes placed within a neural circuit, sending energy and information, into a targeted network. We have conductors that then connect into an energy unit (the battery), as well as electronics to create the pulses and waveforms that go into the device. And these have been largely successful: hundred thousand implants to date, specifically for movement disorders, in our device alone. But we’ve been limited often to stimulation only, and so a lot of this talk is going to be talking about the basics of stimulation (kind of where we are today, where the frontiers are going), but then also starting to fold in ideas of how we make these bi-directional, and add sensing and algorithms to the device.

5. Advanced Bioelectronics So, just very briefly, as a snapshot of where we are today and the opportunity for you as engineers. Here’s just a simple snapshot from our perspective of the commercial opportunities where we have devices serving patients today, running the gamut from movement disorders such as Parkinson’s and tremor, humanitarian device exemptions for dystonia, obsessive compulsive disorder, as well as looking to treatment for chronic pain, all through electrical stimulation or controlled drug delivery through a drug pump. Other emerging areas you may not know about (or actually more in periphery): treating overactive bladder and fecal incontinence – so going in and stimulating the sacral nerve. Then, of course, in development, there are a lot of devices being explored for treatment of depression, epilepsy, as well as looking at migraine; still in the research phases, but of course there’s a member from Cyberonics today, so the vagal nerve stimulator is actually available for the treatment of epilepsy. So what I want to do is give you just one quick snapshot of the potential opportunities. We’ve talked a lot about the heart, and monitoring the heart for performance, for bradycardia, so slow heart beat like a pacemaker, defibrators. Here we’re balancing it with a little snapshot of the opportunities in the nervous system.

6. Improving Design = Balancing Constraints One thing I want to drive home as we get into the talk – and maybe this is my little aside but I think it’s important – is that when we’re operating in this range of design in first circuits for medical devices, it’s more than just the technology. You know, so at ISSCC, we spend a lot of time discussing technology, how do we make it more robust. But when we’re developing these systems and solutions, we really need to keep an eye out for making these systems easy to use, understanding the evidence – the science of how we’re bringing these therapies forward – really understanding as engineers what the true unmet need is: it’s not only just building another bank of arrays of amplifiers or lowering the power and getting a better noise efficiency factor; we really need to step back and say, ‘What is the fundamental problem that we’re trying to solve?’ And then, of course, keeping an eye towards healthcare economics: we need to recognize the times we’re in and understanding how do we bring value to the healthcare system. So in addition to some of the technical details that I’m going to be going into, I want you to remember that context through the rest of the day, especially as we hear from the next speaker about some ambulatory units, the work I’m going to talk about, some implantable devices. What is it we’re really trying to bring forward and provide as a solution to the broader healthcare environment?

7. Major Trend in Biotechnology So this is my snapshot for the talk and almost for the entire day; when I step back and say, ‘what is it we’re trying to achieve?’

There’s really a trend towards, and I put it in quotes, “Smart” Bioelectronic Systems; and what does that mean? Well we take the idea from the intelligent agent – from artificial intelligence if you will, as one potential framework – where we have the nervous system – this could be the cardiac system. We have a system of actuators – the effectors – they’re going to impact that environment. We want to marry to that sensors that tell us what the state is, give us some idea of what is going on within that environment. And then we’re going to work to connect the two: how do we most optimally drive those stimulators with sensors that are picking up and estimating the state of the system? We have to remember that in this environment it’s not just the technology, we also have to think about the physician, the healthcare provider who is operating as a critic, and we can use them, actually take advantage of their capability and know how through learning elements and training to optimize that algorithm. And so the next speaker is going to really get into the development of the algorithms, so I’ll just touch briefly on it, but let’s not lose sight of the fact that connecting the sensor to the stimulator through an algorithm is really critical, one of the most fundamental problems we have. So in terms of outlining my talk, we’ve heard about the tissue interface, I’ll just be touching very briefly on that. We’ve heard and received a great overview from Farit on sensors, and I’ll just give a very brief overview of that as well in my talk. But what I’m going to primarily to focus on is the generation of stimulators, and then how we could hook those together with sensors and start to explore research concepts for future devices.

8. Example of a Bioelectronic Closed-Loop System So here’s an example of a reflex system that we’ve developed for the spinal cord, it really gives you the elements of this smart system. So in a spinal cord stimulator one of the challenges is that the electrode is fixed with respect to the spinal cord – the spinal column – and then as a spinal cord moves with posture changes, the patients would feel a variation and stimulation effect because there’s a motion with respect to electrodes, the volume of activated tissue could be variable. And so in the essence of that smart system framework, we could build a 3-axis accelerometer, hook it to the stimulator, and then generate a simple classifier to say which direction is up, how active is the patient, and then connect that sensor to the stimulator through a control policy. So what is the action you take when the patient changes their posture? It’s a very simple concept for reflex, but it’s really the essence of a lot of the concepts that we’re going to go through over the next hour.

9. Consider Actuation First “Effecting” the Physiology So let’s go ahead and get started within this framework and really dive a bit deeper into the design of stimulation circuits, something that we haven’t really talked much about today.

10. The Appeal of “Actuation”

So first motivating it. You know, I’d really like to talk about stimulation and why it’s important, we talk a lot about sensors, but why is stimulation critical? This a gentleman who has essential tremor, and you can think of it almost intuitively as though he doesn’t have enough phase margin. So as he’s going here through his activities of daily living, you can see he’s struggling: so here he’s trying to draw a spiral, and you can imagine how hard it is for him, say, to sign his name, or to write down a list. As he goes about trying to give himself a glass of water, something you don’t even necessarily think twice about, you can see he’s struggling with that activity and taking care of himself.

11. Considerations in Actuator Design So the reason I talk about this and show this video is, he doesn’t need help diagnosing necessarily his condition. No, he doesn’t necessarily need that sensor - that can help to augment things. But a real opportunity is to do something about it. So that’s why I really want to balance this talk in the tutorial today with stimulation. And what does it mean to do stimulation? There are many elements you have to think about; so going back to that x-ray and now looking at a cartoon version of it, so here’s the device, the electrodes and the connections, and we step back as a system and say, what is the information we need to provide to the nervous system in order to have an optimal effect? What is that information content? What are the interfaces that are critical? So we got a good introduction from Eric this morning. Now what are all those interfaces that are critical in the design? And finally, what is my energy and how do I manage that energy flow through the circuit?

12. Balancing Constraints… And what’s great about this is that if we do it right, we can have a positive impact on patients. So this is the same gentleman and now on the left is his prior video, and on the right, he has a stimulator turned on. And so this is – I like to show this, it’s really the power of getting in, trying to provide a therapy to patients, and this is the engineering motivation for the next series, why we really want to focus on building stimulators. We want to do something to try to provide a solution to this patient.

13. Primer: Physiology of Electrical Stimulation So we haven’t talked much about first principles, and my goal here – just like Eric apologized to the electrochemists, I apologize to any biologists in the audience – is not to give you a deep dive into biology, but I do want to give you an overview and kind of build up from first principles and intuition of what we’re trying to achieve. So most of you know, the nervous system is a biochemical, bioelectrical, computer from a certain point of view. Very similar to our discussion of semiconductor physics, we have insulators made up of lipids, channels that give us a pathway electrically across that lipid insulator, and then separations of ions. And just like you have diodes and you can build up the Nernst potential, very similar in the biological system, we have a separation of ions that gives us a voltage potential.

So we can go in, and your body naturally provides through voltage and chemically modulated changes in conductance in these channels, it sends signals back and forth and does computations through synapses, through, say, in this case, a modulation of potassium and sodium; we can kick off action potentials, and this is going on naturally as you listen to me talk and processing this information. Stimulation, we can think of as a parallel pathway: so we’re going to go in electrically, and just like your body is kicking off natural signals, we’re going to insert information into the nervous system. So, if you will, override and drive a command signal with the aim of a therapeutic effect.

14. Information and Interfaces So the first step when we’re thinking about this is what’s the information? How do we actually capture the right signals? So as a circuit designer, you need to think about the characteristics of the waveform to capture – to basically activate that neural circuit, and the parameters that are critical for that are the charge that’s delivered, both the amplitude and the duration of the waveform. And another critical parameter in terms of the information we’re sending it, is the frequency content and the pattern that is provide to the nervous system. There’s also some patch up: we talked a little bit about it this morning, about those electrode chemical reactions going on, the nature of the interface. We need to think about the charge balance. And so in addition to driving the nervous system, we also need to think about the management of the electrode interface.

15. Example: Neural Activation Function In terms of the neural activation functions this is an example from stimulating the bipolar cells in a retinal prosthetics. You can see we have characteristics in terms of signal strength in the stimulation duration, so that initial onset of stimulation, and looking at what does it take in order to drive a signal into that cellular substrate? And here are some typical characteristics you can see in terms of the real base; there’s kind of a minimum threshold of stimulus strength. And then also, if you will, as you get into shorter and shorter durations, you run out of gas in your signal strength that’s required to activate and the nervous system becomes asymptotically larger. So a lot of design engineers look for this more optimum point of the rheobase, 2 x the rheobase which is called the chronaxie, and this gives us a bit of a sweet spot for activating neural tissue. But one of those critical degrees of freedom is you can adjust those parameters on the amplitude in the pulse width and try to activate different areas of the nervous system, so at lunch we’re talking about how do get certain selectivity and certain neural substrates. This is one of those degrees of freedom that you can apply as a design engineer and try to optimize the design.

16. Information: Patterns for Neural Coding The other essence of information: it’s not just that initial pulse, it’s also how the patterns are formed and how you encode information.

So this is an example that’s taken from a cochlear implant where you want to replace, essentially replace, the information that’s been lost from the death of hair cells. We know that the cochlea is encoding information as essentially a Fourier transform going down the spiraling cochlea embodied as vibrations in the vascular membrane. So what we do as electrical engineers is create a series of band-pass filters, measure the energy at different frequencies, and then map those to electrodes that are placed in a physiological representation, so then we can provide the appropriate pattern of stimulation that matches the physiological drive. So it’s both – I really want to drive that home – it’s both the characteristic of the individual pulse for selecting certain neural cells or fibers, as well the pattern of stimulation. These are things to understand as design inputs for your circuit.

17. Considering Novel Actuation Paradigms I threw this in with the references because, historically, a lot of neural modulation systems – those systems that I showed you on the slide of all the existing commercial devices – they’ve really been driven by fixed patterns of stimulations, so, almost like a 555 timer or crystal generated oscillator. So one of the biggest areas of research right now is understanding how dithering signals, how driving different patterns of information to the nervous system, might provide additional therapeutic effects. So in particular, the papers of Warren Grill out of Duke; he’s systematically exploring this design space and trying to get a better understanding of how patterns might more effectively treat different disease states.

18. Typical Stimulation Requirements Here’s a quick snapshot, building off, sort of the stimulation side of the table that Farit was sharing for sensors, some of the key specifications from a regulatory point of view: you can see the 60601, as well as some other ISO standards for prosthetics. One of the key things that I want to drive home here is that pacemakers are operating, basically, at a 1 Hz interval, so operating through that low frequency rate on the order of microwatts of energy, then as you get into different types of neuromodulators, you go up to the tens of microwatts, to the hundreds of microwatts, and then for a prosthetic system, you actually get up in the milliwatts and tens of milliwatts system. And so when you’re thinking about the design of a specific system, it’s not there’s a simple energy metric that needs to be applied, you really need to understand the characteristic of the device that you’re trying to build and that will set the fundamental power constraint in your integrated circuit and system.

19. Interfaces: Merging Electronics w/Excitable Tissue So thinking, again, about the interfaces. So we got a great overview from Eric on the considerations that we need apply.

You may be very sensitive to any polarization comment that I have on my slide deck, but, I kind of show the two fundamental designs here that one could consider, and he did a great job of giving the explanation of why most implantable devices that you’ll see really favor the left: looking at the polarized interface, especially platinum-iridium is the workhorse of many neural modulation devices that are implantable, while the cardiac space is shifted away into more of a titanium nitride for a lot of interfaces. But, you know, really getting down, as he stated, to that fundamental issue of how do you transfer charge but trying to avoid any undesirable chemical reaction. We’ll talk a little bit later about opportunities, potentially, for more non-polarizable systems, or at least looking at systems that allow for certain chemical reaction and how those might be exploited. So he touched on this, in terms of some of the considerations, the one that I really want to drive home for the next few slides is, thinking from a stimulation standpoint, we need to be very mindful that these are not necessarily small signal excursions. So, remember, he derived the characteristics of an electrode for very small perturbation around the equilibrium point; while you’re providing stimulation, you might actually take a fairly large step away from that, that equilibrium point, and you need to think very carefully as a designer about what that means for you, both in terms of safety, as well as in the energy that you need in order to drive that charge across the junction.

20. Some Interface Constraints: Multiple Standards So, similar to the safety limits that we talked about DC leakage for sensing, in terms of charge delivery, we have constraints that are put upon us. And these constraints are both in terms of the charge per phase for certain square centimeter of electrode density if you will, and then also looking at characteristics in terms of the charge per phase absolute. One of the struggles, I think, that the industry and academia have today, and Eric eluded to this, is that a lot of this work

21. McCreery: Intracortical and Cortical Stimulation of cat parietal cortex safe to K=1.7 is based on historical data, often taken in an acute setting, and still somewhat empirical. And so it’s hard to say, at all times, from first principles what are the true safety limits, and it’s becoming a challenge as we try to scale systems down to the true micro scale. So, Farit showed a great example of taking a sensing electrode and scaling that down through MEMS technology to get a very intimate connection with the nervous system. As we try to do the same thing from a stimulation point of view, we really need to understand the fundamental limits of charge transfer, and what new constraints are put on us from an electrical engineering perspective. Just because we can drive charge across an interface doesn’t mean we should, we really need to be mindful of the safety limitations. So I would like to refer everyone to a paper from the Journal of Neuroscience Methods. While it’s still a bit old, it’s a very good one in terms of giving you a background if you’re interested in understanding the safe and unsafe limits and, sort of, their historical origins.

This is a paper by Merrill, and then, also, the McCreery group is a real pioneer in this space. It’s really understanding, based on these empirical measurements, what are the regions where we can have safe and unsafe charge transfer, and then we can think as designers what safety limits do we need to put in our integrated circuits in order to ensure that the system will be safe as it deploys into our patients.

22. System-Level: Generating Stimulation Signal So with that context, the last element is thinking about energy, and how do we take energy off of our battery source, then supply that as a driving charge into the nervous system. So, this is just to show the highest level schematic, the concept: if we have a source (generally these are batteries that have been put inside a device), some stored a charge pump – I’ll speak a little bit more to that, it gives us a higher voltage rail – and then we, through a series of switches, and, often times, constant current sources and sinks, we can drive through the electrodes and achieve a charge balance, as we discussed, trying to balance out the net charge through the junction on a cycle per cycle basis. One thing to point out is often times we hear bipolar stim: that means we’re stimulating through two distal electrodes, and then unipolar stim is when we’re returning to, say, the common can. So, of course, all the electrode stimulation is bipolar from a certain point of view; this is just an industry jargon that’s used and can sometimes be confusing, so that’s what people mean. So one of our design challenges is that, how do we apply active circuitry to try to minimize the net DC on those electrodes from the safety perspective, and, at the same time, think about scaling designs for the future. So as we kind of go through the next couple of slides, let’s think about that: what are the constraints that are on us in that design of that integrated circuit?

23. Typical Stimulation Circuit (“Stim Engine”) So a lot of the historical stimulation engines (as we call them), the stimulation circuits, really draw a lot from the basic principles of a current DAC. You can think about the same elements you have in the design of a current DAC, are pretty much applying here. So in terms of coming up with reference current generators, building off of band gaps, R-2R ladders, these are all consistent and generally map over from a historical DAC into the physiological space. Some of the things to think about in the overall design are what is your true precision requirement, in terms of what are the steps in granularity that is required for your application, but then the other one is thinking about the accuracy. Why do we care about accuracy? Well, one of the challenges is that an inaccurate system of sinks and sources, you can end up with a residual charge imbalance. And, so, one of the historical ways that designers have dealt with this – sensitivity charge imbalances – is to put a series capacitor inline. So at the output of your stimulation circuit, between that IC and the electrode, we put in a flocking capacitor; also gives us what we’re talking about: a single point tolerance, so there’s a benefit from there, but also people will rely on that circuit to try to drive a net overall zero transfer DC charge. And that will serve okay, say, on a system that has four stimulation electrodes, maybe sixteen stimulation electrodes.

24. Conversion from Energy Source to Electrode Drive But as we look to the future, towards prosthetics and future scaling, we can think about the integrated circuits and say, is there enough opportunity for precision that those capacitors could be eliminated, and what are the steps that need to be taken? The other element, for thinking about that stimulation chip, is that many voltage requirements in terms of the compliance are up at, say, ten to fifteen volts, but they implanted batteries themselves on the order of, as you can see here, two and a half to four volts, and so we have an issue between what the battery can supply and what the compliance requirements are in the device.

25. Methods of Boosting the Voltage (all used) There’s a whole series of methods that can be applied in terms of trying to compensate to get that battery voltage up and to provide significant compliance to drive the tissue. Historically there’s a lot of interest in inductor base systems with boost conversion, those are very efficient, but they have a drawback, in today’s environment, where they don’t do so well in the MRI. So you have to think about the total life cycle of the patient: they’re not just going to have that stimulator, they may need to go back in for a follow up MRI, how’s that boost converter going to actually respond in a strong magnetic field? So typically, devices are shifting to capacitive methods for pumping, either a capacitive stacking solution where you just charge up all the capacitors and stack them together, which is quite simple, but results in the need for large capacitances in order to store enough energy due to the net series impedance. And then the other approach is to come up with a charge pump which has the advantage of having high efficiency and the ability to have, sort of, one large capacitor, but sometimes can have problems with energy throughput. So it all comes back: there’s not this single ideal solution – I think you’re going to hear that throughout the day – it’s not that there’s a one size fits all solution. You really have to look at what the energy requirements are for your application and design the energy pumps appropriately.

26. Emergent Application: Retinal Prosthesis So that’s a brief snapshot of where we are today. I wanted to motivate some of the more advanced concepts that are coming up and give you a sense of where integrated circuit designers are focusing, and the reasons for that focus. So the area of retinal prosthesis is very exciting: the Second Sight, a United States company, just received a humanitarian device exemption from the FDA this past year. And what they’re trying to solve is a problem where the photoreceptors in your eye die out due to retinitis pigmentosa or macular degeneration. So you lose that transduction capability from light, going into an electrical signal into your nervous system.

So the thought is to go in with an electrical circuit and basically do the job of that transduction, do the translation, from light into electricity. But you can think about the number of receptors you have in your eye: you want to maximize that level, it’s like pixilation on a T.V. or on a screen. So four electrodes is probably not sufficient, can you get up to the level of sixty-four? Can you get up to the level of one thousand? What is the ultimate limit for the number of pixels that are available to the end user?

27. Concept for Retinal Implant This is an example taken from Rothermel’s work, where you can see he has his electrode and the photo diodes, and this gives you that essence of taking a two dimensional stimulator, slipping it underneath, in this case, the retina, and then as light comes down and shines on that array, you can stimulate a signal that’s basically proportional to the photon density, so, if you will, replacing that photo transduction in the retina, with an electrical circuit. But you can see already that there’s a challenge here. There’s kind of two challenges that just leap out at us. The first is the density of this array is so high, it’s going to be very hard to have blocking capacitors, so what can we do about that? Then the other issue we have to think about is that in a bio physical environment, where we have materials and conductors soaking, basically, in a saline solution, we want to minimize any DC bias. So we do not want to have conductors going over long lengths with a DC bias because that will tend to drive corrosion and lead to bio compatibility issues.

28. Retinal Implant Supply Architecture So let’s deal with that net DC issue first. So, Albrecht’s paper, they did a nice job of talking about how in your overall system, they had the two supply lines of VH and VL, and throughout the design, all of those external conductors that would be exposed to the saline solution, were basically AC coupled. So nowhere across the chip and in the supply lines was there going to be net DC potential that might end up driving a corrosive process.

29. DC to AC Converter: Apply for Stimulation So with that design constraint, he was able to go in and actually design a fairly clever output driver where taking the signals from a photo transistor that did the job of mapping light into bias current, he then go in and pass that analog signal to the drive electrode and in one phase, basically, suck current out to excite the tissue,…

30. DC to AC Converter: Apply for Stimulation, Recharge …and then as we discussed in the counter phase, try to get a net DC charge balance, so there’s no net DC going out to that electrode. So I really want to drive home that point: as you have those conductors, seal it with materials sitting in the saline bath of your body. You really want to look at minimizing any net DC that’s exposed.

31. But No room for Capacitors! The other issue is how do you fit capacitors in such a high electrode count system? That is a real challenge. So one of the areas of focus is to try to really work on getting the accuracy and precision of the stimulation chips to a level where you can remove that coupling capacitor and try to do it directly from the integrated circuit itself. So thinking about the models of the electrode: if you have a perfectly polarizable electrode, then perhaps you can use that ideal electrode as a its own holding capacitor and just discharge it itself, however if you’re worried about a parallel charge transfer phenomenon, a faradaic reaction, then you need to think about opportunities where you’re really going in and balancing the charge directly through some more active process. And so how do we get that precision and accuracy or an active process to ensure charge and net charge transfer is zero.

32. Advanced Concepts for Charge Balance: So I have two examples pulled from the literature. One is using the idea of correlated double sampling from Sarpeshkar, and the idea here is during an initiation phase, prior to stimulation of the tissue, you sample and basically create a net feedback loop to ensure that these sink and source drive currents are equal and opposite to each other. And then during the delivery phase, essentially the hold, you can provide a bi-phasic charge. And so the first-order within the limitations of that sampling error and the timing constraints, you can get down to a level of current that potentially allows you to remove the coupling capacitor, as long as you do a good job of ensuring compliance and accounting for all of your error sources.

33. Advanced Concepts for Charge Balance: An alternative approach which is getting a lot more traction these days, and it was just covered in last year’s ISSCC, is the idea of more of an active monitor, if you will. So when we talk about the polarizable electrodes, where we can end up with an residual voltage, we can take that fact that there’s a little bit of a memory process, and more actively monitor and take action to balance out that polarization. So if we see, after a bipolar current stimulation, that there’s a net polarization that’s residual because we’ve driven that electrode out of equilibrium, we can provide counter boluses of charge if you will, to bring you back up to that equilibrium potential. But there are a lot of details to be worked out. You know, pulling from Eric’s talk this morning, we really have to think about how are you actually measuring that potential? Do you have another electrode in the system? Make sure you’re accounting for the impact of that measurement electrode and think about some of the dynamics. What are the limitations of the boluses? Are these actually providing any perturbation to the neuromodulation pattern that you’re trying to provide? All of these details really need to be worked through and fully understood.

34. Emerging Method: Optogenetic Neuromodulation

This motivates, kind of my last point on the area of stimulation, is really looking out towards the future. So we’ve talked about some of these stimulation chips from today, some of the practical limitations, thing about prosthetics is driving us into the nearer term future. What are the things that might be sitting on the long term, what are those new technical opportunities? Well with all these constraints on electrodes and the challenge of getting greater selectivity from simple scaling, a lot of neural scientists and neural engineers are exploring this new space of optogenetics. So the concept of optogenetics is instead of going in and providing electrical stimulation by driving an electrode, instead, you go in and actually transfect genetic engineering, the neurons, so that they carry light sensitive channels. Remember we were thinking about electrical stimulation as a parallel pathway to drive activity in the cell. Here’s yet another opportunity, another option, were we’re going to make this cell sensitive to light and that gives us another parallel path. And we can go in and shine light and, basically, modulate information in the nervous system. No electrode required, however you need a light source. So a different paradigm.

35. Critical Interface Concept: Now what’s interesting about this space – and I put a summary, kind of the latest, a summary paper I wrote with Justin Williams and can go latest status – is that stimulation, electrical stimulation, is primarily an excitatory response locally; so, you know, we drive the neural tissue, we excite it, and then if there’s inhibition, often times that’s downstream, if you want to get inhibition, you can do a nerve block, but that requires some kind of special electrode or a very high frequency stimulation. By selecting certain channels, like a chloride ion pump, you can actually shut down neural activity simply through shining light on the neural substrate. So this is a very unique paradigm in terms of actually getting in a modulated neural activity, one that’s a very keen interest to neural scientists today.

36. Building Blocks of an Optogenetic Stimulator Prototype But the reason it’s still, I think, a bit farther out, is the energy requirement. So when we step back and think about the design of a system that’s going to use light, and optogenetics is a actuation method, we have to think about a lot of details. Practical details, but important details. So one, electrodes and wires are quite flexible, fairly easy for us to manipulate and route to the body, what are the challenges of an optical fibre? How do we solve that problem as an electrical engineer? As an IC designer, a lot of the physical infrastructure is similar between a putative light stimulator and an existing neural stimulator, but the energy requirements are one hundred to a thousand times greater right now for optogenetics, and so all of those charge pumps and those issues are one hundred times harder for us to deal with in this specific case. So while I think this might have potential opportunity in the far distant future, still today, it’s really limited to the research environment.

37. Summarizing State of Actuation -- 2014 But I wanted to give you a sense of what’s, kind of, the state-of-the-art, what’s happening in neural science in actuation in 2014, and what some of those trends are. And so at the highest level, it’s all about specificity and getting new information content. And so when you think about that motivation for optogenetics, and trying to get that channel selectivity, the real goal is to get that specificity, get finer control in the nervous system. So that’s not just going on in the space of optogenetics, it’s also going on in the space of classical neural stimulation. So some of the key trends are electrodes are scaling: they’re getting down to new dimensions of control. In order to get full advantage of those electrodes, we really need to have more independence in the way that we can steer current, and control the fields. As we get more channels though, we have the counter-constraint of capacitors, how do we deal with the capacitor count and try to eliminate those? That’s really putting new levels and requirements on the levels of accuracy and precision from our stimulation engine. The other area of interest is getting out of the classical fixed patterns and looking at new domains, new patterns of stimulation, and of course that’s motivating the idea of closed loop and responsiveness, instead of just relying on patterns that are, say, Gaussian, or Poisson distribution, just based on, still, an open loop concept.

38. Consider Sensors (and Algorithms) Is the real essence and opportunity in something, a device that can take advantage of real time changes in the estimated state of the nervous system, and dynamically adjust the stimulation accordingly? So that’s going to motivate the next series of the talk where we talk about sensors.

39. What are the Signals for Detection? So what are the signals available for detection? And we talked a bit, this morning, about electrical sensing and the opportunity there, but we have to remember that the body itself is also sending signals through a transmitter, neural transmitters.

40. Circuit Scaling: Cells-Circuits-Networks So there’s a potential there as well for tapping in and measuring the chemical environment of the brain, the nervous system, not just the electrical property. So as an intergraded circuit designer, the first thing you have to ask yourself is what’s the scale of the nervous system that you want to interact with? So just like we’re designing, say at ISSCC, and we have discussions at the level of the gate, and doping, to the level of the full chip, as well as networks in between, the same question arises time and time again in the design of a neural modulation system or any biophysical system. What is the scale with which you want to interact with the body?

41. Quick Primer: Origins of Bioelectrical Activity

Is this going to have profound effects? This is actually a pretty serious design decision. So just like we were talking about earlier, we have these channels that basically cross the lipid membrane and give us the buildup of electrical activity. And then the fluctuation of those channels through the voltage sensitivities or chemical sensitivities, those are what’s encoding the information in the nervous system. We can go in and try to tap into those signals, both of the electrical and the chemical signals, and you use them at different scales to try to derive different estimations to the state of the nervous system itself. I just want to say this is the last and final example of the Nernst potential; so you’ve had three examples today, both semiconductors, electrode design, and then the operation of the nervous system itself, so it’s all the same fundamental equation.

42. Cellular Scale: Origins of Bioelectrical Activity So one of the key questions is, with this activity that’s flowing through your nervous system, through the electrical signals going back and forth through these ion channels, how do you tap into that? And so, Farit was saying a differential potential measurement is required, and that’s true, but another key design input is to think about where those electrodes are going to sit. So it’s not just enough to have the bipolar measurement, where are you actually going to place those electrodes to get the proper vector with the proper sensitivity for the measurement that you’re trying to take? So this is an example of, say, measuring extra cellular – an extra cellular signal at the level of the single neuron, where we have a single axon, where we have a good alignment to the bipolar electrode, so as an action potential goes by and we get the wave of activity, we can actually pick off that field potential fluctuation as a single spike.

43. Ensemble Level: Origins of Bioelectrical Activity If we go up in scale though, to the level of networks, say cortical networks, or basal ganglia networks, then we start to look at the ensemble behavior, not just the single cell, but the ensemble behavior of the network and the characteristics of the signals that we see there, and those can be quite different. So once again, we are looking at continuous flow lines of current, but because of the nature of the large networks, they have a certain change in the signal characteristic. And Farit gave an example this morning, I have the same image in my slide, of these oscillations and the spatial frequency characteristics, and trying to understand the nature of the activity within the nervous system.

44. Sensing ECoG and Local Field Potentials I’m going to give you a specific example from our research to try to drive home some more intuition into the nature of the signals themselves, and actually correct a couple of misnomers that we’ve heard today about what’s going on inside of the brain.

So this is an example taken from a primate where we have the one electrode over the motor cortex; so we’re measuring activity where the primate is planning motion, and then another electrode is placed over his shoulder muscle. We’ve designed the device so that it triggers on the onset of motion measured as the change from an EMG from a low level signal to a high level signal, and then we take the snapshot of what was going on in the brain just prior to motion, and then during motion itself. If you look at the raw time domain signal, it looks a bit like hash in both cases; it’s very hard to differentiate what exactly is going on. But if we switch to more of a spectral view, plot the energy – the spectral energy – over different frequencies, you can actually see a pretty clear signal at rest around the area of 20 Hz; so this is the putative beta signal. And then as the primate starts to move, it desynchronizes and goes away. And so it’s this event related de-synchronization that’s one of the hallmarks of network activity in the motor planning circuits. Now why is this interesting?

45. Example of Network Coding Well if we look for the same signals, let’s say in a Parkinson’s patient, how might those change? How might we use those as a biomarker? So this is an example recorded acutely from a Parkinson’s patient when they’re off their medication. And so that same biomarker, that beta band, is showing an elevated level of energy, and kind of that thick state. And it seems that – literature seems to be showing that – there’s a correlation between an excess amount of energy in that band, and symptoms such as akinesia, inability to move, or bradykinesia, slowed down motion. So in this case, in this specific example, the patient took their levodopa, so their synthetic dopamine, and then started to take effect here, and as their symptoms were alleving, we actually saw a relaxation in that beta band energy more to a normative state. So this is what’s kind of interesting, this is a new area of sub cortical signals that are representing information about patient’s state, potentially, as fluctuations in the frequency time characteristics of the field potential itself. The thing I want to drive home is that these are small signals. So, you know, we are hearing about in the brain, we don’t need to worry about small signals, microvolt level, you know, we can get away with noisier circuits and lower power. That’s not necessarily the case. When you get below the cortex, and in sort of the ganglia networks and into the thalamus, we’re actually seeing signals drop back down again to the order of microvolts, and even sub microvolt levels, when we’re coding and looking for information on the state of the subject. And so, I just want to warn all the integrated circuit designers, be careful that we don’t design noisy amplifiers and say that there’s no information there because we made a presumption that the signals themselves were large. That’s something that we want to be very careful about and mindful as we look towards the future.

46. Bioelectrical Signals: Amplitude and Frequency Characteristics

So to sum it up, I have the figure as well from Farit, where we have the nature of potential signals coming from EMG, ECG, EEG, local field potentials, and then of course the single cell. Each of them have their characteristics in terms of amplitude and frequency. The one thing I wanted to point out through this figure is as we get more and more knowledge into some of the underline circuits in the brain, some of our assumptions about signal levels and their amplitudes in terms of field potentials and ECoG might be questioned, so you want to keep an open mind on what the ultimate noise floor resolution might be in a future system.

47. Constraints: Impact of Electronic-Tissue Interface So now, after the potential opportunity of bioelectrical sensors, let’s pivot to another opportunity, and that’s in the space of chemical sensing. And so we talked briefly this morning about polarizable and nonpolarizable electrode systems. Some folks are saying there could be a potential opportunity by looking for chemical reactions in an electrode, and using that for a biomarker itself; so, if you will, to supplement the bioactivity and have a look at it for sensing. So, stepping back, you know, for the constraints on the system. Just like we’re talking about for the polarized and non-polarized electrical interface to the nervous system to our integrated circuits, we have a similar set of constraints as well on the sensing. In terms of thinking about distortion, the evoked response, any concerns about polarization, residual polarization after stimulation, and how do we mitigate those issues.

48. Bioelectrical Measurement Principle Right at that front-end, in terms of how we’re talking, we talked about differential amplification, the need for a good differential amplifier, and the thing I want to drive home here is that in many implantable systems that we’re dealing with, we do not have a bias electrode. So the system is floating, we need to find some other way in order to bias it up, and so we have high impedances basically to all of the electrodes, we don’t necessarily get to use a driven leg electrode; it’s one of our constraints.

49. Operational Transconductance Amplifier So from my point of view, just summarizing the state-of-the-art, and, what Farid was sharing earlier, is that it really does come down to a choice of electrode based on your target requirements. And so I see when I’m studying the field and seeing what’s coming about, it really is that split between the operational transconductance amplifier and the capacitive interface, like we discussed; certainly for those applications where 1/f noise is acceptable.

50. Chopper Modulated Instrumentation Amplifiers And then looking for the applications where we really want to go to the most precision, if you will, the highest accuracy at low frequencies, how do we actually obtain that through chopper stabilization? And so the issue that we have, as Farit was bringing about, is that choice and constraints of a…

51. Instrumentation Amplifiers …Baseband amplifier versus the external chopper architecture, where the gains and the common mode rejection ratio are essentially, in my experience, and it’s the same to first-order. We might get a potential benefit from the 1/f noise improvement, but at a pretty steep tradeoff in terms of the input impedance characteristics of that chopper amplifier. So one of the big questions that you have to ask yourself in the design selection process is, are my electrode characteristics, coupled with my signal characteristics, driving me towards one design choice over the other? And that’s really, in our case, where we spend a lot of our design time. It’s really understanding the nature of that electrode interface, and in the case of our systems with fairly large electrodes, that impedance is low enough that chopper stabilization is still acceptable. However, in the case of scaling down to these more miniature electrodes in the future, chopper stabilization may really run into problems with the limited input impedance that the amplifier has itself.

52. What are the Signals for Detection? So now we jump into the area of chemical detection. And so, we’ve seen that we can measure the field potentials, Farit talked about measuring ECGs, what are the other potential signals that are available to us that we can potential servo a device in the future? And that’s where we turn our attention and look at the area of neurotransmitters, so starting to look at signals such as dopamine that’s shows in Parkinson’s disease. Is there a way that we can detect dopamine and use that signal for some good effect?

53. Making the Electrode Work Harder for You? So this is where we come to the other nature of the electrode interface, where we start to look at detecting the chemical reactions that might occur at the interface itself, and then extracting that into useful information to estimate the chemical environment.

54. Neurotransmitter (NT) Detection So the mechanism by which we try to go in and extract information on neural transmitters, there are a lot of potential opportunities. Those could go from chemiluminescence, looking at liquid chromatography, as well as PET (so positron emission spectroscopy), a whole gamut of potential imaging approaches, but where the opportunity, you know, real interesting opportunity sits within the device space is in electrochemical detection. So it’s more direct, it’s fast from the standpoint of we can measure things on the order of hundreds of milliseconds to seconds, and still get the sensitivity in the potential for integration that we would like to see in a future device.

55. Electrochemical Transduction So in terms of methodologies, as you probably well know, there’s two common approaches. One is the constant potential amperometry, where the idea is we bias an electrode and then measure

the current that flows at a specific electrode potential. It is a simple and fast approach, however it really lacks in the chemical selectivity that we need, unless you go through the trouble of electrode functionalization. So another area that’s getting a lot of attention these days in the research realm is fast-scan cyclic voltammetry. And the idea here is to go and provide ramps of voltage over time, and then we can extract based on the current and charge profile, estimations of the environment by looking at what exact charge is flowing at a specific potential.

56. Examples of Dopamine Sensing w/ FSCV@CFM The challenge there is that it can be a little bit more difficult to integrate than a simple summing amplifier. So here’s an example pulled from Mohseni of dopamine sensing. And so here’s the basic intuition as we have a voltage that we ramp over time; we of course get displacement currents that flow, but then what we can see is, if we zoom in with enough precision and accuracy, that’s superimposed on top of that displacement current as a small faradaic current that occurs at specific potentials. And so what we can do is go back and de-convolve where that charge transfer occurred and use that as an estimation for the chemical environment.

57. System on a Chip So one of the areas that Pedram is really pioneered is in the area of integrating these kind of systems together. So here’s an example of a chip, and we’ll be coming back to this presented in 2013, that the idea is to combine that generation of a waveform to drive the carbon electrode, and then and at the same time, sense the currents that are flowing and telemeter that information out real-time to get information about the dopamine environment surrounding the electrode. So what we’re seeing is just like we’re achieving large scale integration and bioelectrical amplifiers, similar opportunities are starting to coexist in the field of chemical sensing.

58. Sensors Wrap-Up 2014 So in terms of the state-of-the-art for sensors in 2014, we’re still faced with the challenge that a lot of our signals are low bandwidth from the modern circuit perspective, certainly from what you see at ISSCC. In the presence of those small signals and low bandwidths the issues such as 1/f and offsets can become a real issue, and so we’re still interested in chopper stabilization. However, countering that, is as we scale down to finer and finer electrode geometries, the input impedance of the chopper amplifier is probably going to become more and more of an issue. So we have an interesting design tradeoff dilemma between the finite input impedance of the chopper amplifiers, with the benefits that they bring with 1/f mitigation versus that very high input impedance of a capacitive amplifier, but with potentially the tradeoff of 1/f noise. So what I see right now is the solution to that dilemma is actually going back to the architecture that Farid proposed, which is more of chopping and using amplifiers that have, in essence, source falling, so that we’re only limited by the residual capacitance, parasitic capacitance of the amplifier input itself. So that seems to be a nice tradeoff to that dilemma where things stand today.

Another opportunity is that we don’t just need to rely on electrical signals: people are starting to look at the opportunity of fusing chemical information. We also talked about inertial sensing: what can we do to combine these different modalities together to get a better estimate, better specificity in terms of understanding what’s going on in the nervous system environment? One of the challenges we face, though, in building a closed loop system, is how these two interact? How does the stimulation circuit couple over and impact the sensor? Something that’s not talked about very often. We tend to talk a lot about the design of the stimulator, the design of the sensor, but we don’t talk about what happens when, say, we drive 5V at 120 Hz into the nervous system while still trying to resolve a microvolt level field potential.

59. Sense-Stim Interface Considerations That’s what I what to spend a little bit of time on now. So sense-stim interactions have been around for a long time, especially looking at the cardiac pacing environment where there is an electrode in the heart, we’re providing it stimulation pulse, and then the device itself looks to ensure that we’ve captured the heart. So we stimulate and then we look: have we effectively captured the heart? Now the benefit of that system is that the signals themselves are separated in time. So when you stimulate and you measure, even though the signals themselves are overlapping the frequency space, they’re well separated in time. And so that was a design trick where we can just basically use blanking. You can blank out your sensing channel, wake it back up after the stimulating event, and say have I effectively captured the heart? The challenge in the brain, and looking at the neural modulation space, is that a lot of our signals are driving at, say, at 100 Hz, 120 Hz, and so in the time domain, they’re overlapping with each other. So it’s very hard to just blank the channel because essentially the stimulation and the signal we’re trying to sense are superimposed on top of each other temporally. But one of the things that we can take advantage of is that they’re separated in frequency in a lot of cases. So the design space that’s available to us is different, and we have to exploit that. Another challenge we face as push into the next generation of devices is that pacemaker signals are on the order of millivolts while a lot of the brain interface devices are actually on the order of microvolts. So the signals are dropping by three orders of magnitude. And our stimulation voltages, though, are staying abruptly the same. So we have one volt simulation signals, we’re trying to resolve microvolt signals proximal to the stimulation contact.

60. Current Sense & Stimulation 1: So historically, like I said, in pacemakers, they could deal with this to a large part by just using a blanking control. So they could take advantage of the fact that because the evoked potential was coming milliseconds after the stimulation, they can just shut off the sensor, and then reawaken it at the end of the stimulation cycle. And then the only thing that was left to manage was essentially residual stimulation: artifacts due to the recharge and charge injection. And that does work very well. So if you have a system, say within evoked potential, using blanking is a very good first-order mitigation to coupling a sensing and stimulation system together.

61. Characterization of Disease Process Establishing the “Transfer Function” Like I was saying, as we’re finding in the brain as we go and characterize transfer functions, what people are finding is that these small signals are really coincident. And so I have an example here, taken from a recent publication by Peter Brown, where here’s that beta signal, the one we talked about earlier, here’s time going across, and what you notice is that as the stimulator amplitude is turned up, it eventually hits a critical threshold. And when it hits that level, the beta band drops down, and it seems to correlate as I was saying earlier, with relief of the patient’s symptoms. Now the trick is that in this case, that stimulator is pulsing at about 120 Hz, and so it’s always on, always providing background level stimulation, in this case, on the order of three to four volts. So in this case Peter’s solution is not to go and blank the channel, it’s instead to take advantage of the fact that this signal is at 15 Hz, while the stimulation is about ten times faster. So just like a radio, as long as we can avoid channel saturation, perhaps we can apply frequency selectivity to still pull this signal out from the background.

62. Generaling Approaches to Minimize Stimulation Like I say, it’s really down to maintaining that signal so we can still take advantage of pulling it out. And what I’m finding, and what a lot of the field is finding, is that it’s not just a simple silver bullet, where you say, what we’re going to do is just blank the channel, or we’re going to build a perfect preamplifier with high common mode rejection ratio. Instead it’s stepping back and solving this problem systematically through the entire signal chain. So in this case, thinking about the tissue electrode interface, thinking about the design of the sensing channel and how it can be properly biased, as well as taking advantage of potential opportunities in the stimulation circuit as well as, as the last step, algorithmic mitigation.

63. Sense Chain Consideration So the first step is really right at that front-end, thinking about the tissue electrode interface. What can you do to maximize the probability that you can detect the signal if you have a good differential amplifier? Well one thing, is to try to get as much of that stimulation signal presented as a common mode input. And so a lot of electrode designs are really focused on trying to maintain symmetry, if you will, between the sensing dipole and the stimulation vector. And so this was first presented by Rossi, and then there was a follow up paper by Stanslaski, they really worked on sensing and stimulation on electrode architectures that try to, as much as possible, present a uniform stimulation artifact to each input of the differential amplifier, so by careful choice in symmetry and the sensing of stimulation electrodes. Of course in reality, this is limited by the mechanical accuracy and the tissue impedance fluctuations that are seen practically within any neural circuit or any other biophysical circuit. But at least as a first principle, this is a great place to start.

64. Improving Concurrent Sense & Stim Performance

The next thing that’s been discovered is a lot of those same advantages that we see with the driven leg electrode in a cardiac signal, apply here as well. So we can actually servo that floating system if you will, because it’s not exactly floating. As Eric pointed out earlier, and Farit, we have these large resistors that are actually kind of keeping things biased. What can we do to servo where the net system sits to minimize that residual impact of the common mode? So just like we can servo in a cardiac space at a distant leg electrode to minimize the common mode, we can do the same thing here by measuring the residual common mode step seen at the amplifier and then biasing where the sensing system sits relative to the stimulation circuit, and minimize that residual. And we get the same sort of benefit, as I’ll show here.

65. Common-Mode Minimization with Concepts Shown So this is an idea of our noise floor. The goal is to maintain about 100nV/rtHz with the chopper amplifier and the measurement range from about 1 Hz out to 100 Hz. On the onset of 5V of stimulation at 120 Hz, what happens is that large perturbation driving into the sensing chain and the random fluctuations that come with that tend to elevate the noise floor up, essentially an order of magnitude. So even though we’ve gone through all this effort to design a low noise chopper amplifier, we’ve completely undermined it by turning the stimulator on. And so a lot of those signals that we’re trying to observe are now amassed from us.

66. Improving Concurrent Sense & Stim Performance: But by applying that same concept of a common mode servo loop, going back, just like we saw with the driven leg electrode,

67. Common-Mode Minimization with Concepts Shown we can control and minimize that common mode step and bring it down to a level that we can once again maintain the acceptable noise floor design by the sensing chip. So my point here that I really want to drive home is that a lot of those historical techniques that served us well over the years in other applications can be bootstrapped and brought forward into these new sensing paradigms.

68. Sense Chain Saturation Control: The last area to think about in terms of sensing front-end is to think about what can we do to soften that final edge. So even though we go through the role of symmetry and the tissue electrode interface and the electrodes that we use applying common mode feedback, we’ll still be left with a small residual signal, and one thing we can do there, and the final thing, is to apply a little bit of band-pass filtering to soften up the edge, and that helps to avoid any distortion in any nonlinear folding that might occur in the preamplifier back into the baseband.

69. Algorithm Mitigation of Residual Stimulation Interference

The last thing I want to really drive home here, and, sometimes it’s forgotten in the design of integrated circuits, is that we can apply algorithms as well. So we don’t need to necessarily lean completely on the design of the integrated circuit to solve our problems. We can also look to algorithm design because these are deterministic signals: we know when the stimulator is on, we know when the stimulator is off. So we can characterize that environment, we can characterize the residual signals, the impact it’s having on our sensing chain, and feed that information forward and take counter measures in the classification and control policy. So here’s an example from some animal research, in this case stimulating and sensing from the hippocampus of a sheep, where we have two burst of stimulation. So in the first burst of stimulation, you can see a carryover effect, which is labeled as the after-discharge that is not present in the second stimulation. So we’ve actually biased the stimulation right to the level where an after-discharge is probable about 50% of the time. Now the issue with an after-discharge is that this basically the induction of a seizure-like event in the network itself, so we want to avoid this. So in an ideal world we would detect the induction of this event and take immediate action in that closed loop system. The problem is that this signal is on the order of 10µVrms, and the stimulation itself, presented to the hippocampus, is on the order of 4V. So that can be tough to resolve. You can kind of ask yourself, I’m building a simple detector and if I just look at the energy level that’s floating in that band of the hippocampus, kind of drawn by the blue box, it’s hard for us to differentiate whether the stimulators on, or whether we’re actually in a seizure state. And so what we can do is actually train the detector, identify the different states: looking at seizure when the stimulation is off, seizure when the stimulator is on, no seizure when the stimulator is on, no seizure when the stimulation is off; so four potential states. And then you use that information to create a bit of a smarter detector. So we can use that additional degree of freedom - of an estimation in the stimulation power - to pull out what the actual state is. And so, if you will, it’s the tilt to the measure, so we can add that extra degree of freedom and this helps us to separate the state where there’s a seizure but the stimulator is off, and there’s no seizure but the stimulator is on. You know, that’s not a very effective detector if it’s just measuring every time you’re turning on your stimulator. So we have that then demonstrated here, the final detection, where we have actually induced the seizure and then, basically, within a second, we’re able to detect that presence and start to take immediate action in the justice stimulation itself. So my key point through this is that as you start to develop sensors and you start to then hook them to actuators, we need to be really mindful of the impact of putting them together and what are the potential areas where they can cross couple and what are the mitigation pathways that we can take to address those shortcomings.

70. Summary Example: Practical Bioelectronic Enablement Of Closed-Loop Neural System And if you go through systematically and kind of study the solution and, say, address it at the design of the tissue electrode interface, through symmetry, adjust it at the sensing amplifier through appropriate filtering, through common mode feedback techniques, as well as taking full degrees of freedom and the design of the classifier and the control policy, you have a chance, potentially, of actually marrying these systems together and fully achieving a closed loop. But

really come at it as a total system problem, not just trying to make one specific block and hit it out of the park.

71. General Framework for “Smart” Systems So my final wrap-ups here are to give three examples of case studies that are kind of the-state-of-the-art right now where designers have done a pretty good job of showing proofs of principle, of interfacing with the nervous system, stimulating it, sensing how that stimulation is changing the nervous system, and then applying a useful closed loop for some potential therapeutic benefit. I really want to drive this home, these are all still very investigational, still in the research stage, but they’re looking promising.

72. Brain-Machine-Brain Interface So each of these examples is drawn from a pretty exciting area called the brain-machine-brain interface, and it’s really about closing a loop, if you will, around a network in the brain, so some area of the nervous system. So that we’re going to measure activity, make an estimate of that activity, try to correlate it to some disease state or some equivalent neurological state, and then stimulate to try to take appropriate counter-measures to nudge it back to a more desired outcome. So examples where this is being explored are in deep brain stimulators for Parkinson’s disease, they’re being explored in brain modulation for epilepsy – in fact there’s a new device that’s just been approved in the United States that’s a responsive neural modulator, so it’s embodying some of these principles – as well as looking for systems for the treatment of paralysis and brain injury. So all these applications, with the exception the epilepsy device, which has just been approved in the United States, these are all still investigational but are showing a great deal of promise.

73. Adaptive High-Frequency Stimulation DBS Concept So let’s start with the area of Parkinson’s disease ‘cause we keep coming back to that beta band and its use as a potential biomarker. A group in 2013 showed a very interesting proof of principle where what they do is go into the electrode, where they were going to provide chronic stimulation, and then following that symmetry principle, they measured differentially across the stimulation electrode what the field potential was. Looking for that biomarker, that energy in the beta band, once it achieved a certain threshold of power, they would then turn on the stimulator. They would keep the stimulator on until that beta threshold fell below critical value and it would shut off. So your mental analogy of the control system is essentially a thermostat where the energy in the beta band of the field potential is an analogy to temperature, the stimulator is like the furnace, any time that beta band crossed the critical threshold they would turn on the stimulator, any time it would cross down below they’d turn it off. Very, very simple control loop, not very complex at all.

74. Adaptive HFS DBS Concept – Typical Sequence So just to make sure you really get it, the essence here is here’s the signal coming from the deep brain target, the basal ganglia circuit. This is where I want to drive it home, these are peak-to-peak signals on the order of a microvolt, so these are small. These are not large signals. As you get into the subcortical signals, be prepared for very challenging noise floor requirements. Once this level would burst up above a critical threshold, the stimulator would be turned on, and once it fell below the critical level, the stimulator would turn off. Now these ramping characteristics here, this is actually one of those important physiological elements, that’s there so that the patient can actually tolerate it: the neural network does not respond well, you can actually feel the side effect if you give a discontinuous step to the stimulation itself. One of the strengths of this study and why I pulled it out as an example is that they ran a series of no stimulation, they gave closed loop stimulation, they compared it to the classical continuous stimulation paradigm, but then this is also critical, they then ran a selected pattern that was generated during closed loop through, but uncorrelated with the sensor itself. It’s one of those things you have to ask yourself as you’re giving a closed loop, any closed loop signal to the nervous system, is it the pattern that’s actually causing the effect, or is it the fact that your being responsive? Does that make sense? Because as I’m closing the loop, I’m actually getting this chatter from the adaptive DBS, and maybe I just happen to give a nice pattern, maybe what the nervous system really likes is not the fact it’s responsive at all, but just I’m dithering the signal a little bit, and you need to actually separate those two outcomes from each other.

75. Adaptive DBS:– Results from Pilot Prototyping So this is the summary of data from Peter that he provided, and I’ll start on the right. This is what has everyone excited about the field is that if you look at the time on percentage for stim over time, he’s seeing a systematic drop off. And so crosses pilot patient cohort, he was seeing on average a reduction in energy of about 50% with adaptive stimulation on. And that’s a big deal. Remember, these are right now with a primary cell battery, when your battery depletes, you have to go in for surgery and have it swapped out. So extending the longevity of the device has real impact on the patients. The other thing that’s enticing about this is the trends, where it’s appearing to get better over time. And one of those questions is, is there a synaptic plasticity effect? Is this the kind of effect that over time the nervous system will learn, and it will get better and better? Very exciting area for future research. The unexpected result, that no one really thought was going to happen, was that pilot experiments suggest that with adaptive DBS the patient’s symptoms were also improved beyond what they were getting with classical therapy. So people, engineers had always hypothesized that we’d see in that energy reduction, but no one was really expecting a better outcome for symptom control. And in this pilot paper he did actually see some statistically significant improvements. So this is a very hot area in terms of building up closed loop neural devices based on this pilot work, and there are multiple teams now going to replicate it and look for potential opportunities for translation in the future.

76. Future System-on-Chip (SOC) Architectures The other area that’s getting a lot of attention and excitement is in rehabilitation. And so this is an example drawn from Mohseni’s work in the potential treatment in traumatic brain injury and stroke. And the idea here is that where this circuit block sits, there has been a lesion or some other damaging insult to the nervous system, and the goal of the integrated circuit system is to bypass it, if you will, in some intelligent manner. So sense activity within the neural substrate, process that information, and then deliver downstream excitatory signals with the role of basically replacing functionality within the network. And so I show this as an example of the key block diagrams in terms of building the smart system, the sensing front-end, the DSP controller integrated in terms of doing the classification of spikes and then choosing the appropriate timing (this decision circuitry), and then the stimulating back-end. It has all those elements that we’ve talked about through the talk.

77. Analog Recording Front-End I just showed a snapshot here of his analog recording front-end where, once again, he’s got the capacitive differential amplifier that we’ve talked at great length about today, really for his characteristics of the signals, looking at the spikes and trying to have a very high input impedance; this is the ideal decision choice. And then, of course, just some final signal conditioning for filtering and driving the analog to digital converter.

78. Digital Signal Processing (DSP) Unit So we’re familiar with this by now. The next step is actually classifying those signals, coming up with a simple state machine, looking at different threshold crossings to actually analyze the perturbations in the signal and say yes, I’ve just detected a spike on a certain electrode, and then passing that information on to the stimulator.

79. Stimulating Back-End The stimulating system back-end also has all the features we’ve discussed. There’s the servo loop control to get better compliance, so we drive current out to the electrode site; there’s a biasing circuit to make sure that the electrode has a net DC set point and keeps to make sure the

stimulations sinks and sources are biased to an appropriate level; and then of course opportunities for rebalance either actively through an active recharge mechanism or using the residual capacitor that’s coupling the system to do a passive discharge.

80. Miniaturized System Assembly So all the elements you’ve seen integrated together into a miniature system that can be put on top of a rat. So here’s his ASIC. A couple of the other features that I wanted to drive home is that for the recording amplifiers, because it’s using those capacitive inputs with the high impedances, you can go right into the ASIC. However on the stimulation side, there are these 220nanoFarad decoupling capacitors. So we’re still relying on those to ensure net DC balance; of course those are quite large. So it’s okay for a four channel system, but we’d have challenges scaling that up to, say, a thousand channel system. And, finally, running off a battery, little coin cell, needs to have a boost converter to get that up to drive the compliance in the system, just like we talked about earlier. I like this as an example for also bringing home all the key elements of the circuit blocks that we’ve talked together, but integrated as a system.

81. Behavioral Recovery After TBI So here’s what’s interesting and promising in this work – it was just published in the Proceedings of the National Academy in December – is that the rats were given a lesion insult at day zero, and then the circuit was tested with either a control, open loop stimulation, or an adaptive pattern – basically, tuning in and trying to do an IC functional replacement for that functionality (or, you know, replacement is a bit of a stretch, basically doing a responsive stimulation based on the activity that was measured just upstream of the insult). So what you can see here is that the outcome, which is basically reaching success – so these are the rats reaching for a pellet – there’s this statistically significant improvement of open loop stimulation versus the control, but also adaptive stimulation over both. And so, while the control rats were recovering on the order of 20% of their reaching success, with the adaptive stimulation circuit they were approaching on the order of 70% improvement. This is a pilot study, but it really gets into that opportunity that we might have as electrical engineers to design systems that are helping to aid the nervous system and functional recovery. So, potentially, a very exciting space for us to exploit in the future.

82. Electrochemical System Architecture

The final system I want to loop back to, because we spend so much time talking about bioelectrical systems, is a potential opportunity for electrochemical systems. And this is another parallel effort within Pedram’s lab, looking at servoing stimulation to drive a certain level of chemical neurotransmitter levels into a distant part of the brain. So in this case, he’s stimulating the medial forebrain bundle of the rat brain based on the level of dopamine that’s measured in the working electrode. And so through this closed loop system, he can potentially servo to a set point as another degree of freedom of neuromodulation.

83. Circuit Architecture: Microstimulator So the key points here, just looking at the microstimulator circuit, as once again, I just want to tease out one other thing is looking at the binary levels in the controls, is the essence of these DAC stimulators really drawing on the principles you’ve already learned in the design of any DAC controller. So a lot of that existing know-how really maps one-for-one into the design of these biophysical systems.

84. In Vivo Experiment – Anesthetized Rat So here’s an interesting result from the bench testing with the anesthetized rat where in the little tick marks you can see stimulation pulses that are given; in black is the measured response of the dopamine; and then in the hash lines, this is getting towards his model, if you will, of a servo controller. And so the long term goal here is to come up with a system that’s measuring background levels of neurotransmitter, and then can apply stimulation with a control loop to really drive and maintain that in a biophysical relevant manner. So a very intriguing area and potential future space for closed loop neuromodulation systems.

85. Final Considerations So the final consideration, getting back to overall framework, I just want to drive home for you as engineers is how do you test these circuits?

86. Validating the Signal Chain

So what are the mechanism’s that are available to you? I put in a couple of the references to databases that are out there today that have been fully annotated, these include both cardiac and EEGs, so especially seizure libraries. So if you have a new circuit idea and you want to test it out I would encourage you to go to these databases and download the annotated and you can test out your ideas and run them against other people’s designs to get a better idea for how well your design’s performing versus others.

87. Closing Thoughts… Some final closing thoughts is when we think about actuation of biological systems, why I think this is so important is that really the goal for a lot engineers in this space is functional restoration, this is really a strong bias for trying to address the issues, facing patients, and their caregivers with some kind of compensatory technology. And, as you can see, a lot of actuation methods are maturing to the point where they might have potential; so both the scaling of existing stimulation systems today, as well as some concepts of the future, like optical stimulation, or working on stimulation to control electrochemical signatures in your body. In terms of the sensors and interfaces, there’s that balance, always, in terms of power and performance, and as Farit was making reference to, and I really want to drive home, a lot of effort today is going into fusing multiple sensors together for specificity where you can combine different modalities and combine that information together for a better estimate of the patient’s state. And then finally, it really comes down to a total system solution; that’s why I spent the time up understanding what are the mitigations that you have to take when you put a sensor and a stimulator together and try to get them to work together as a cohesive unit. With three examples taken from the recent literature, showing the opportunity within that space. Thank you very much.