motor neuroprosthetics bci

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THE EMERGING WORLD OF MOTOR

NEUROPROSTHETICS: ANEUROSURGICAL PERSPECTIVE

Neurosurgery. 2006 Jul;59(1):1-14

Author(s):Leuthardt, Eric C.; Schalk, Gerwin; Moran, Daniel; Ojemann,

Jeffrey G.

Jonathan Pararajasingham

ST2 Neurosurgery


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Introduction Machines that could be controlled by one's thoughts.

Brain computer interface devices (BCI) detect and translate neuralactivity into command sequences for computers and prostheses.

Electrodes recording from the brain are used to send information tocomputers so that mechanical functions can be performed.

BCI devices aim to restore function in patients suffering from loss ofmotor control e.g. stroke, spinal cord injury, multiple sclerosis (MS)

and amyotrophic lateral sclerosis (ALS).

BCI will broaden repertoire of neurosurgical treatments available topatients previously treated by non-surgical specialists.


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TechnologicalEvolution

1970s: research that developed algorithms to reconstruct movements frommotor cortex neurons, which control movement

1980s, Johns Hopkins researchers found a mathematical relationshipbetween electrical responses of single motor-cortex neurons in rhesusmacaque monkeys and the direction that monkeys moved their arms (basedon a cosine function).

1990s: Several groups able to capture complex brain motor centre signalsusing recordings from neurons and use these to control external devices

Early working implants in humans now exist, designed to restore damagedhearing, sight and movement.

The common thread throughout the research is the remarkable corticalplasticityof the brain, which often adapts to BCIs


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Review Aims

Important for the neurosurgeon to understand: what a brain computer interface is its fundamental principle of operation salient surgical issues when considering

implantation.

To review the current state of the field of motorneuroprosthetics research, clinical applications,and the essential considerations from aneurosurgical perspective for the future.


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BCI PRINCIPLES

machine that can take some type of signal from the brain andconvert that information into overt device control such that itreflects the intentions of the user's brain.

In essence, these constructs can decode theelectrophysiological signals representing motor intent.

With the parallel evolution of neuroscience, engineering andcomputing technology, the era of clinical neuroprosthetics isapproaching as a practical reality for people with severe

motor impairment.


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Terminology

Commonest technical term for these types of devices is a braincomputer interface (BCI). Other terms include:

motor neuroprosthetics direct brain interface

brain machine interface

neurorobotics

OUTPUT BCIs = devices that convert human intentions to overtdevice control

INPUTBCIs = devices that translate external stimuli such as light orsound into internally perceived visual or auditory perceptions


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Input BCIs: Sensory prostheses

AUDITORY PROSTHETICS most successful example of sensory prosthetic is the cochlear

implant. lack the cochlear hair cells that transduce sound into neural activity.

Auditory implants are also being extended to direct stimulation ofthe brainstem for those with dysfunctional cochlear nerves (e.g.NF2)

VISUAL PROSTHETICS also making significant inroads into clinical viability Prosthetics have been applied to every aspect of the visual system

cortical implants (both surface and intraparenchymal electrodes)

optic nerve stimulators

retinal (both subretinal and epiretinal) implants

Each of these platforms undergoing clinical trials


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Practical and clinically viable BCI now deserves serious

consideration due to:

improved understanding of the electrophysiologicalunderpinnings of motor related cortical function

rapid development of inexpensive and fast computing

growing awareness of the needs of the severely motorimpaired

Essential for the neurosurgical community to understandwhat these devices are and their implications towards

patients.


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BCI: A device that can:

decode human intent from brain activityalone.

create a completely new output pathway for the brain.

change electrophysiological signals from mere reflections ofCNS activityinto the intended products of that activity: messages and commands thatact on the world.

change a signal such as an EEG rhythm or a neuronal firing rate from areflection of brain function into the end product of that function

replace nerves and muscles and the movements they produce withelectrophysiological signals and the hardware and software that translatethose signals into actions.


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Detecting and

convertingneuronal signals

in to electrical

signals


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Feedback Mechanism & Adaptation

As a new output channel, the user must have feedbackto improve theperformance of how theyalter their electrophysiological signals.

Continuous alteration of the neuronal output matched against feedbackfrom the overt actions (same for learning to walk, complex movements,

etc.)

Subject's output can thus be tuned to optimize their performance towardthe intended goal.

Brain must adapt its signals to improve performance, but also BCI mustadapt to changing brain to further optimize functioning.

This dual adaptation requires a certain level of training and learning curveboth for the user and the computer.

The better the subject AND computer are able to adapt, the shorter thetraining required for control.


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Practical ElementsFour essential elements to the practical functioning of a BCI platform:

1)Signal acquisition, the BCI system's recorded + digitised brain signalinput.

2)Signal processing, conversion of raw information into device command: Feature extraction = the determination of a meaningful change in signal Feature translation = the conversion of that signal alteration to a device

command.

3)Device output, the overt command or control functions produced.

word processing, communication, wheel chair, prosthetic limb. new output channel, therefore must have feedbackto improve how they

alter their electro physiological signal.

4)Operating protocol, the manner in which the system is turned on/off.


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1) Signal Acquisition real-time measurement of the electrophysiological state of the brain.

usually via electrodes (invasive or non-invasive).

Common types of signals include: Electroencephalography (EEG) (from scalp) Electrocorticography (ECoG) (beneath the skull) Field potentials (within the parenchyma) Single units (microelectrodes monitoring individual neuron AP

firing)

Other possible signals: include MEG, fMRI, PET, and optical imaging(not practical currently).

Once acquired the signals are then digitized and sent to the BCI systemfor further interrogation.


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2) Signal Processing Feature extraction = pulls significant identifiable info from the gross signal.

Signal translation = converts that identifiable info into device commands.

Process of converting raw signal into one that is meaningful requires statistical

analysis.

These statistical methods assess the probabilitythat an electrophysiological eventcorrelates with a given cognitive or motor task.

BCI system must recognize that a meaningful, or statistically significant,alteration has occurred in the electrical rhythm (feature extraction) .

Associates that change with a specific cursor movement (translation).

Signal processing must be dynamic such that it can adjust to the changing internalsignal environment of the user.


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4) Operating Protocol

This refers to the manner in which the user controls howthe system functions.

On or off, controlling feedback speed, command speed,switching between various device outputs.

These elements are critical for BCI functioning in thereal world application of these devices.

Currently, very controlled research parameters set (i.e.researcher turns the system on and off, he or she adjuststhe speed of interaction, or defines very limited goalsand tasks).


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NEUROSURGICAL ISSUES OF BCIS

Besides the processing issues that define therequirements of a BCI system, there is a separate set

of factors that a neurosurgeon must consider whenconsidering application towards a clinicalpopulation.

neurosurgical community should have a framework

to evaluate these new systems as they apply topatients.

Safety, Durability, Reliability, Consistency, UsefulComplexity, Suitability, Efficacy


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Evaluation

Safety

Assessing the risk is relatively straightforward as they willmost likely utilize variants of standard technical procedures(deep brain stimulators, cortical stimulators for pain, andplacement of grid electrodes).

Durability/Reliability:

construct design, scar formation

removal and re-implantation in short periods around areasof eloquent cortex could potentially increase the risk ofinjury.


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Com

plexity of control How complex the control afforded by a given BCI can be assessed by how many

degrees of freedom (DOF) of control there are.

Degrees of freedom refer to how many processes can be controlled in parallel(=dimensions in space).

Clinically viable BCI requires a minimum of 3 dimensional control, or 3 DOF.

1 dimensional control (1 DOF) = binary interaction (e.g. yes or no)

2 dimensional control (2 DOF) = moving a cursor on a screen along an x and yaxis.

3 dimensional control = controlling an object in thee dimensional space (such asa basic robotic arm) or controlling an object in two dimensional space with aparallel switch command function (i.e. mouse with a click function).

7 dimensional control = For more physiological approximations of limb functionsuch as controlling a robotic arm (i.e. 3 shoulder, 1 elbow, 1 forearm and 2wrist).


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Speed and Accuracy function with a minimum of errors which could potentially lead to

dangerous situations

variables are incorporated into a single value = rate of information

communicated per unit time, or bits per minute, or bit rate.

The bit rate of a BCI system must increase as the complexity of choicesincreases.

The current bit rate for human BCI systems are approximately 25bits/minute. This translates to a very basic level of controlbeing able

to answer yes and no, very simple word processing, etc.

The information transfer rate for an effective BCI system that reliablyand quickly responds to the user's environment will need to be higher.


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Suitability Patients may all have some type of motor impairment but they may require very

different device outputs relevant to their clinical situation.

A SCI patient may optimally benefit from a device that allows the individual tocontrol some type of motorized wheel chair or allows them to control their bowel and

bladder sphincter tone.

An ALS and locked-in stroke patient, however, might have needs primarily related tocommunication.

An amputee may need very fine control of a prosthetic limb.

A motor cortical related implant may be optimal for a subject with cord dysfunction

or amputation, but may not work well in an ALS or stroke patient where that part ofthe brain may not be normal.

Vital that the patient population and its underlying pathology be taken intoconsideration for what type of platform may be used and what functions it provides.


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Technical vs. Practical

Technical demonstration refers to the first time that something is technically possible:

Fetz in 1971 demonstrated that one degree of control could be obtained from the operanttraining of a monkey to alter the firing rate of a single neuron

Wolpaw et al. in 1991 - single degree of freedom control in human BCIs demonstrated usingEEG signals. Leuthardt et al. in 2004 - electrocorticography

Practical Demonstrations: demonstration in real world use:

Single unit based systems developed by Donoghue et al. now being commercialized by thecompanyCyberkinetics (BrainGate Neural Interface System)

In 2002, Serruya et al., using microelectrode arrays in monkeys, were able to achieve two-

dimensional control . Three dimensional control accomplished by Taylor et al. in 2002through the use of microelectrode s in primates

When applied clinically to the first human subject, preliminary reports seem to indicate thatcontrol has been somewhat limited despite optimal results in previous primate paradigms

Whether this is due to the subject being in a less controlled environment, a limitation of thesignals acquired, or simply due to the early nature of the human trials, not clear at this point.


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1. EEG-Based Systems

Human BCI experience until recently has beenconfined almost entirely toEEGrecordings

studies have mainly evaluated the use of

sensorimotor rhythms, slow cortical potentials,andP300 evoked potentials derived from theEEG.


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EEG based BCI platform


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Sensorimotor Cortex Rhythms

In awake individuals, primary sensory or motor cortical areastypically display812 Hz EEG (called activity rhythm) when theyare not processing sensory input or producing motor output

Beta activity is typically associated with 1826 Hz beta rhythms.

Movement or preparation for movement is typically accompanied bya decrease in and beta activity over sensorimotor cortex.

Most relevant for BCI operation, this decrease in activity also occurswith imagined movements, and does not require actualmovement.

People, including those with ALS or SCI have learned to control or[beta] amplitudes in the absence of movement or sensation


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Slow Cortical Potentials

Slow changes in EEG potentials that are centered at the vertexand occur over periods of several seconds.

Negative SCPs are usually associated with movement andother functions involving cortical activation

Positive SCPs are usually associated with reduction in suchactivations

Shown that people can learn to control SCP amplitude

This system has been tested extensively in people with late-stage ALS and has proved able to supply basic communicationcapability and control over simple Internet tasks.


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P300 Evoked Potentials

P300 potential distinguishes the brain'sresponse to infrequent or significant stimulifrom its response to routine stimuli.

Donchin et al. have used P300 potentials as the

basis for a BCI.


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EEG-Based Systems - Limitations

no companies currently attempting to market a BCI platform using EEG.

brain signals acquired with this method are susceptible to external forces(i.e., electrode movement) and contamination (i.e., interferencegenerated by muscle movements or the electrical environment).

less fidelity and spatial specificity and a limited frequency detection(


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Single Unit Based Sy

ste

m1

Studies on modulating activity of single neuron for control wereperformed in non-human primates in the early1970s

early studies were limited to one dimensional control

1980s: Georgopoulos developed method of decoding 3D handmovement direction from a population of neurons in primary motorcortex of non-human primates

By serially recording the single-unit activity from 50200 individualneurons during a repeated reaching task, an accurate prediction ofaverage hand movement direction was madepost hoc.

During the 1990s, neurophysiologists refined and enhanced theseneural decoding methods to include prediction of both 3Ddirection and speed (i.e. hand velocity)


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Single Unit Based Sy

ste

m2

In the late 1990's several groups were having success in recordingchronic, single unit action potentials from a number of neuronssimultaneously which culminated in a number of papers (~ 2002)showing elegant multi-dimensionalBCI control.

The proximal arm area of primary motor cortex is thedominant structure targeted for BCI control via single-unit activity.

Movement data fits well with a cosine function (demonstratedexperimentally with monkeys).

This process first proposed by Georgopoulos is the basis for alllinear decoding methods used in single unit BCI research.


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Single Unit Based System 3

There have been some limited trials in which single neuronal firinghas been used in quadriplegic subjects to achieve control.

Cyberkinetics involved with the development of this signal platform.

They have currently implanted four patients and are open forfurther recruitment of subjects.


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Single Unit BCI System:

A. Consists of 10 10 array of microelectrodes.

B. Array attached by cable that transmits signals to Connector.

C. Connector is then externalized through skin and connected via

external cable to signal processor.


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Single Unit Based System - Limitations

best signal for BCI control has been achieved with multiple, single-unit action potentials recordedin parallel directly from cerebral cortex, in terms ofaccuracy, speed and DOF thansingle unit data.

Limited microelectrode technology, thus obtaining long-term stability of single unitrecordings has proven difficult.

Current single unit recordings techniques require insertion of a recording electrode into thebrain parenchyma.

Given the highly vascular nature of the brain, it is impossible to implant such a device withoutsevering blood vessels and hence inducing a reactive response around the implant site

Tissue encapsulates the implanted microelectrode via a standard foreign body response.

Over time, microelectrode becomes electrically insulated from the surrounding tissue and can nolonger discriminate action potentials.

Unlike stimulating neuroprosthetics electrodes (e.g. deep brain stimulators), increasingstimulation current to counter encapsulation does not work.


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From a clinical point of view, it should give a neurosurgeon significant

pause to implant microelectrodes into the brain of patients knowing thatthey will only provide a year of BCI control.

Since constructs prone to scarring and would be implanted in eloquentregions of cortex, repetitive procedures could have significantdetrimental effects to the patient's long term functional and cognitivestatus.

Invasive BCI electrodes, therefore, need a prolonged life span to warrantthe risks of an intra-cranial procedure.

To date, current single-unit microelectrodes have long-termbiocompatibility issues leading to limited life spans.

However, there are several groups developing new biomaterials as wellas slow-release drug delivery systems that could decreaseencapsulation.

E.g. dexamethosone on the microelectrode might reduce the initial injuryresponse


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ECoG Based Systems2

BCIs based on EEG have focused exclusively on and beta rhythmsbecause gamma rhythms are inconspicuous at the scalp.

In contrast, gamma rhythms as well as and [beta] rhythms are

prominent in ECoG during movements.

TheECoG signal is much more robust compared to EEG signal(5x magnitude, finer resolution, higher frequency).

Higher frequencybandwidths, unavailable toEEG methods,

carry highly specific and anatomically focal information aboutcortical processing.

These are more prominent at electrodes that are closer to cortexthan EEG electrodes and thereby achieve higher spatial resolution.


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ECoG Based Sy

ste

ms3

Recent studies have cogently demonstrated its effectiveness as asignal in BCI application.

Leuthardt et al. in 2004: Over brief training periods of 324minutes, four patients mastered control and achieved successrates of74100% in one-dimensional tasks.

Schalk, et al. in 2004: demonstrated two dimensional online controlusing independent signals at high frequencies inconspicuous to thatappreciable byEEG

Leuthardt et al. in 2005 demonstrated that ECoG control usingsignal from the epidural space was also possible.

Such studies show the ECoG signal to carry a high level of specificcortical information which can allow the user to gain control veryrapidly.


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ECoG Based Sy

ste

ms4

Beyond the technical demonstration ofECoG feasibility, there is some evidence tosupport the implant viability of subdural based devices.

Studies investigating tissue responses in subdural placed electrodes have beenmore encouraging.:

Subdural electrode implants for motor cortex stimulation shown to be stableand effective implants for the treatment of chronic pain.

Preliminary work using the implantable Neuropace device for the purpose oflong term subdural electrode monitoring for seizure identification also shown tobe stable.

ECoG is a very promising intermediate BCI modality because it has higher spatialresolution, better signal-to-noise ratio, wider frequency range, and lessertraining requirements than scalp-recorded EEG

lower technical difficulty, lower clinical risk, and probably superior long-termstability than intracortical single-neuron recording.


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CONCLUSIONS

Currently, research is only beginning to crack the electrical information encoding the informationin a human subject's thoughts.

Understanding this neural code can have significant impact in augmenting function for thosewith various forms of motor disabilities.

Each of the reviewed signal platforms has the potential to substantively improve the manner inwhich patients with spinal cord injury, stroke, cerebral palsy, and neuromuscular disorders,interact with their environment.

Computer processing speeds, signal analysis techniques, and emerging ideas for novelbiomaterials

The field of neurosurgery will have the potential to move from a purely ablative approach to onewhich also encompasses restorative techniques.

In the future, a neurosurgeon's capabilities will go beyond the ability to remove offending agentssuch as aneurysms, tumors, and hematomas to prevent the decrement of function.

Rather, he or she will also have the skills and technologies in their clinical armamentarium toengage the nervous system to restore abilities already lost.

motor neuroprosthetics bci

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