Prototype Closed-Loop Deep Brain StimulationSystems Inspired by Norbert Wiener

Jeffrey Herron1 and Howard Jay Chizeck1,21Department of Electrical Engineering

2Department of BioengineeringUniversity of Washington

{jeffherr, chizeck}@uw.edu

Abstract—Implantable neurostimulators have rapidly becomeestablished methods of treating a variety of neurological disor-ders. The development of implantable neural interfaces enablethe testing of Norbert Wiener’s hypotheses regarding neuraldisorders and their relationship to ideas of cybernetics. However,currently deployed medical devices of this kind are open-loop. Forexample, DBS for treatment of tremor does not take into accountthe variable and intermittent nature of the tremor. Closingthe loop through sensors and real-time communication to theimplanted neurostimulator could result in lower average powerdissipation and reduced side effects from unneeded stimulation. Inthis paper we present a closed-loop DBS platform for investigatingcontrol strategies for the management of essential tremor. Wedemonstrate a system capable using a variety of sensors includinginertial measurements, electromyography and neurostimulatorelectrode readings. This sensed data is used to modify stimulation(within limits pre-set by a clinician), thus resulting in a closed-loop system.

Keywords: cybernetics, neural engineering, deep brain stim-ulation, essential tremor

I. INTRODUCTION

In the first half of the 20th century, Professor NorbertWiener revolutionized the way engineers approached controland communication problems by conceiving a new field ofstudy: cybernetics. What made this truly innovative was hisnotion that this field could not only be applied to the mechan-ical and electrical systems that engineers of the time had beenstruggling to design, but to the biological nervous system aswell. Wiener viewed the brain as an extremely complicatedinformation processing system that is bound by the samemathematical equations found in control systems, such as thosethat govern stability through feedback.

Of particular interest here are his writings on the nature ofintention and Parkinsonian tremor, both of which he believedto be due to a problem with neural feedback circuits [11].In particular, he theorized that intention tremor was due toa disruption in proportional mapping between proprioceptivesensory feedback and muscle tone. He and his colleagues evendemonstrated how these oscillatory behaviors can arise in arobotic analogue by modifying feedback gain [12].

Now, 50 years after his death, engineering and techno-logical expertise have caught up with Wiener’s ideas. Manyresearch groups are now investigating methods of Brain Com-puter Interfaces (BCIs), which can connect the neural system

to computational devices. These have a multitude of researchpurposes and have also found their way into clinical practice.Implanted neurostimulators have become an accepted methodfor modulating brain signals for the treatment of severalneural disorders [4]. While the mechanisms are not yet fullyunderstood, deep brain stimulation (DBS) has been shownto reduce tremor for both Essential Tremor and Parkinsonsdisease [4]. We now have the technological foundations toinvestigate Wiener’s theories on the nature of tremor in thebrain, and any improvement of these treatments will rely onthe field of cybernetics.

In this paper, we describe prototypes of closed-loop DBSsystems. These prototypes are a first step toward using sensingand control paradigms for closed-loop systems for essentialtremor patients. Closed-loop DBS for essential tremor willenable stimulation to be delivered only when it is necessary,lowering power consumption and reducing exposure to anyside effects. Furthermore, we anticipate that these closed-loopneurostimulation systems will provide a platform for futureinvestigations into various applications for implanted neuralinterfaces in humans.

II. BACKGROUND

Essential tremor is a progressive neurological disorder thatcauses uncontrolled rhythmic movement most often in thehand and arm of a patient. Diagnosing essential tremor can bedifficult due to the multitude of other movement disorders thatexhibit similar symptoms. Clinicians may use limb mountedaccelerometers to precisely diagnose and characterize earlysigns of essential tremor [3] and in research a combinationof limb accelerometry and electromyography have been usedto study the nature of essential tremor [10]. Like many otherneurological disorders, the severity and nature of essentialtremor can vary significantly across different patients. Thereare four main types of tremors that have been observed:rest tremor occurs under no load and no movement; posturaltremor occurs while the limb is suspended against the force ofgravity; kinetic tremor is caused by any volitional movementof the affected limb or body part; and intention tremor oftenoccurs during visually-guided volitional movement [3]. Themost common tremor diagnosed is kinetic tremor, which isoften observed in the hand/upper limb, but tremor can occuranywhere in the body. The cause of tremor is not fullyunderstood, but a surgical lesion or electrical stimulation of thethalamus have a dramatic effect on the amplitude of the tremor,which potentially suggests the development of an oscillatory978-1-4799-4562-7/14/$31.00 c© 2014 IEEE

Cerebral ControllerΣ Desired Position Error Muscular Commands Skeletal Muscle System

Position Feedback Stretch ReceptorsProprioceptive Mapping

Fig. 1. Muscular Control Model inspired by Wiener’s descriptions inCybernetics [11]

circuit that is disrupting normal function [10].

While previously considered a benign neurological dis-order, essential tremor can often have a debilitating impacton the lives of the affected population. Patients exhibitingsevere tremor often have a significantly harder time completingeveryday tasks [5]. Furthermore, the presence of tremor hasalso been correlated with an increase in the number of misstepsexperienced during walking [7], which poses a danger to thepopulation who exhibit these symptoms.

Tremor can often be treated with medication. However,drugs are usually not sufficient to stop the progressive natureof the disorder [5]. For severe cases of essential tremor, deepbrain stimulation of the ventral intermediate nucleus of thethalamus has been shown to be an effective treatment [10],but the tremor must be of a significant impairment to warrantneurosurgery. It has been found that DBS will not only reducethe overall magnitude of the tremor, but will also increase thefrequency of the oscillation [10]. However, the effectivenessof the deep brain stimulation can be hampered by unintendedside-effects which may require lower than desired stimulationcurrents to be used. Side effects are often dependent on thestimulation parameters selected, and can include sensationsof burning or tingling on the skin, speech problems, or eveneye closure [8]. Numerous studies and literature reviews haveshown that DBS is a safe and reliable method for treatingtremor with fewer side effects than other surgical procedures,with an additional key benefit of being reversible [4] [9].

Since the mechanism for tremor is poorly understood, themechanism by which DBS mitigates the tremor is thereforepoorly understood as well. Most hypotheses tend to focus onthe potential for stimulation to disrupt the oscillatory musclecommands being sent to the peripheral muscles [4]. This dis-ruption could occur by exciting the central oscillator to operateat a higher frequency, or by suppressing the potential oscillator,which would allow the normal high frequency muscle com-mands to pass freely [10]. By mapping the control system interms of Wiener’s tremor hypothesis as described in Cybernet-ics [11], illustrated in Figure 1, we can potentially gain furtherinsight into this treatment. For instance, Wiener believed thatthis disorder could have arisen due to the excessive gain ofproprioceptive position feedback driving the muscle systemto behave oscillatory [12]. Deep brain stimulation could bereducing the gain of this feedback system, making the systemmore stable and thus reducing oscillatory behavior. Due to theillusive nature of many of the questions relating to the brainand neural control methods, these hypotheses remain largelyunconfirmed. Regardless of the mechanisms involved and theunderlying theory, DBS results in a lower amplitude and higherfrequency tremor observed from the accelerometers and EMGused to characterize a patient’s tremor.

Fig. 2. Demonstration Overview and Block Diagram: Sensors (not shown)send data to a desktop PC (1) running prototype control algorithms. The PCcontrols the USB connected Nexus System (2). The Nexus communicates tothe neurostimulator (3) through a magnetic coil (4). The stimulators electrodes(5) are connected to an oscilloscope (6) to display the current output.

Many of the existing shortcomings of deep brain stim-ulation come from the open-loop nature of the implanteddevices [6]. The vast majority of commercially available DeepBrain Stimulators are non-rechargeable. They require invasivesurgery to replace the device (although not the electrodes inthe brain) whenever the battery runs out. The battery’s lifespanis determined primarily by how often the device is used, and atwhat level of stimulation. Without being able to directly sensethe onset and level of the tremor (and take appropriate action),deep brain stimulators are set to continually stimulate thepatient. This reduces the battery life, and may cause undesiredside effects due to unnecessary stimulation. Some patients havemanual adjustment of the stimulation levels (e.g., during sleep).However, these are awkward to use and can get in the way ofeveryday activities. Additionally, such ’patient programmer’devices are unavailable to many patients with essential tremor.

By including sensors and controllers to close the loop,adjusting stimulation in response to the presence or absenceof tremor and its degree of severity, we can design stimulationparadigms that can result in lower overall power usage andpossibly less exposure to stimulation side-effects, while stillsuccessfully suppressing tremor. Existing DBS for essentialtremor does not take into account the intermittent nature ofthe disorder. By using a closed-loop system driven by directsensing of tremor, stimulation can be limited to only theperiods when it is actually needed for tremor suppression.

III. METHODS

A. System Overview

We have built an open ended test-bed consisting of anarray of possible sensor types, all communicating with a single

PC that manages a communication channel to an unimplantedneurostimulator. An overview of this prototype system isshown in Figure 2. The neurostimulator we are using is aMedtronic PC+S, which is capable of both stimulation andsensing from lead electrodes [1]. We utilize a modified patientprogrammer provided by Medtronic (Brain-Radio/Nexus) tosend a variety of commands to control the stimulation andaccess sensed biopotentials from the electrode sites. Thisprototyping platform allows for different control paradigms tobe considered. Sensor data is streamed in near real-time to alocal computer, which then modulates stimulation parametersthrough a defined control algorithm.

B. Control Paradigms

The first control approach investigated uses the sensed datato trigger a pre-stored stimulation pattern. In this situation,a clinician determines the appropriate electrical stimulationparameters to use, but the closed-loop system is responsible fordetermining when the stimulation is being delivered. This canbe triggered by either sensing tremor as it is being experiencedby the patient, or after sensing the intention to move inan effort to stop tremor before any oscillation manifests.Utilizing EMG to trigger a static stimulation pattern has beenpreviously explored [13]. Our prototyping system is a moregeneralizable platform, including other sensors and allowingfor more sophisticated control algorithms.

The second control method is the dynamic modificationof stimulation parameters in response to attributes of sensedsignals, such as magnitude and spectral components of thesensed tremor. In this case, a clinician defines a range ofparameters and stimulation limits. This permits the designof a control loop using sensed tremors to adjust stimulationparameters. Initial experiments were limited to the use ofproportional control, for ease of implementation while alsodemonstrating the platform’s ability to prototype dynamicforms of control.

C. Sensing Methods

We are considering three signal sources for prototype de-velopment for sensing the biophysical manifestations of essen-tial tremor. These include measuring the physical movement ofthe limb, the electrical signals from the muscles, and the neuralsignals commanding the muscles to activate in an oscillatorymanner. We can measure physical movement and muscle dueto tremor using a wrist mounted inertial measurement unit(IMU) and electromyography (EMG). The ability to use thesesignals to predict tremor onset has been shown [2]. Sensingthe oscillating neural circuits commanding the muscles is farmore difficult and requires invasive biopotential measurements.This can be accomplished by utilizing neurological readingsfrom the cortex or thalamus through the DBS electrodes. Byfusing relevant data from these sensors, we will estimate theinstantaneous level of tremor that the patient is experiencing.From this estimate, we can use the Medtronic Nexus systemto trigger stimulation or to modify stimulation parameters, inreal-time.

The IMU watch is a battery-powered device that directlymeasures the tremor that a patient is experiencing by sensingthe accelerations and position of the affected arm. It fea-tures a full nine axis IMU, which consists of a three axis

accelerometer, gyroscope, and compass. Each axis provides anorthogonal and independent data source for us to use in tremorstate classification. This is probably more information than isnecessary, but we plan to determine the minimum number ofaxes needed to accurately sense tremor state. Other features ofthe IMU watch prototype include a wireless connection to alocal computer to stream data to.

For EMG sensing, we are using the gTec Mobilab. Thedevice has eight sensing channels that are attached to the armutilizing disposable adhesive gel electrodes. The electrodesare distributed around the arm over various motor groupsof interest. Furthermore, the sensed data can be wirelesslystreamed via a Bluetooth connection to a local computer. Weare also developing a custom EMG armband to replace thegTec. This will be integrated with an IMU sensor.

Of particular interest is the possibility of utilizing the im-planted lead electrodes of the DBS system for neural sensing.While one electrode pair is configured to provide stimulation, asecondary pair could be used for neural sensing of the tremor,allowing a single implanted system to perform closed-loopmitigation of tremor. This has the advantage of not requiringany additional worn sensors or utilizing precious battery poweron packet transmission. Since electrical stimulation at theelectrode sites is able to mitigate tremor, it seems plausiblethat there may be a biological signature of tremor that we cansense from the electrodes as well. Fortunately, the MedtronicPC+S has the ability to sense and transmit electrically senseddata to the same outside communication system, thus allowingus to demonstrate this capability.

IV. RESULTS

We have implemented three scenarios to demonstrate thecapabilities of the system. Each scenario was tested by an able-bodied individual mimicking oscillatory tremor movements.The first scenario uses the IMU watch described above totrigger a pre-set stimulation pattern. We used the axes ofinertial measurement to determine a magnitude measurementfor both total limb acceleration and rotation. Applying theFFT to these signal magnitudes allows examination of thetotal power within the frequency band range of essentialtremor (4-12 Hz) [2]. These power measurements are thencompared against thresholds to determine when to turn onthe stimulation. When the power of the tremor band dropsbelow the threshold for five seconds, the stimulation is turnedback off. A time-synchronized plot of one of the accelerometerchannels, band power estimation, and resulting stimulatoroutput is shown in Figure 3. In this experiment the stimulatorcircuit was kept in the off state until triggered, which resultedin an observable artifact around 6 seconds and 16 seconds fromthe stimulator circuitry switching on. The implanted devicealso has a built-in slow start mechanism to limit the changein the peak pulse voltage to limit patient exposure to suddenchanges in stimulation current, which is observable in the firstfew seconds of active stimulation.

The second scenario uses an EMG based proportionalcontroller. In this experiment we performed a FFT on a singlebiopotential EMG channel, collected through the commercialbioamplifier with a wet electrode. We then mapped the totalpower within an identified tremor band to the stimulation

Fig. 3. Inertial triggering of static stimulation patterns: In the top plot, the raw acceleration magnitude is plotted with two tremoring motions made atapproximately t = 5s and t = 15s. These tremors increased the spectral power within the tremor band shown in the middle plot. When this power raises abovea threshold, it triggers a pre-set stimulation pattern until tremor subsides.

Fig. 4. Electromyography mapped proportionally to stimulation voltage: In the top plot, the raw EMG is plotted with two tremoring motions initiatedat approximately t = 1s and t = 6s. These tremors increased the spectral power within the tremor band shown in the middle plot. This power estimation isproportionally mapped to the stimulation voltage level, plotted in the bottom graph.

voltage with a static gain. A time-synchronized plot of thecollected EMG, band power estimation, and resulting stimu-lator output is shown in Figure 4. The total power that theneurostimulator is consuming is clearly proportionally drivenby the level of measured spectral power density within theidentified tremor band. There appears to be some lag in the

proportional controller, likely due to communication delaysbetween the computer and neurostimulator.

In the third scenario, we demonstrate a system that utilizessignals sensed by the neurostimulator. Since we cannot yetdemonstrate this capability in an implanted patient, we used asignal generator connected across two of the four electrodes as

Fig. 5. Neurostimulator electrode spectral power mapped proportionally to stimulation voltage: In the top plot, the raw signal generator output is shown.In this experiment, a dramatic ramping up of the sensed signal to 80Hz was used as our surrogate for a neural signal of interest. This ramping function beginshere around t = 4s. The signal generator then held the output at a high frequency before slowly ramping back down to the steady state, which is achieved aroundt = 12s. The spectral power of the sensed signal is shown in the middle plot. This power estimation is proportionally mapped to the stimulation voltage level,plotted in the bottom graph.

a surrogate for a biopotential signal of interest. A noisy sweepfunction that rested for long periods at one hertz before rapidlyramping up to 80Hz before slowly ramping down was used asa stand-in for the biosignal of interest (indicating tremor). Wecalculated the spectral power within a band centered around80Hz to implement a proportional controller to actively adjuststimulation voltage. This is shown in the time-synchronizedplots illustrated in Figure 5. It is important to note that thefirst plot is data recorded directly by the neurostimulator andstreamed to the PC over the same channel that we sendcommands over. Streaming both sensing data and commandsover the shared communication channel is likely responsiblefor the lag seen in the time it takes for the stimulator to adjustparameters in response to our control actions.

V. NEXT STEPS

The above systems are demonstrations of the technologicalfoundations for a cybernetics based closed-loop deep brainstimulator. However, these technological demonstrations arenot fully representative of the closed-loop dynamics due tothe lack of the most central piece: the patient. We are nowcollecting IMU and EMG data from essential tremor patientsfor offline analysis for the purpose of building diagnosticfeature recognition. We need to identify the features within thesensed data that can be calculated in real-time, so as to drivethe closed-loop control system. This will then be implementedin patients who already have a DBS implanted in order toexperimentally evaluate the performance of different closed-loop control approaches.

This work represents a first step towards implementinga closed-loop deep brain stimulator in human patients. Asthese systems are clinically deployed, we anticipate that large

amounts of data will be obtained. This will facilitate dynamicalmodelling that will give new insight into the neurologicalbasis of tremor and will expand the understanding of theunderlying neural control problems that interested ProfessorNorbert Wiener.

ACKNOWLEDGMENT

This work is supported by Award Number EEC-1028725from the National Science Foundation. The content is solelyresponsibility of the authors and does not necessarily representthe official views of the National Science Foundation.

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