Download Signal acquisition and analysis for cortical control of neuroprosthetics

Signal acquisition and analysis for cortical control of
neuroprosthetics
Stephen I Helms Tillery1 and Dawn M Taylor2,3
Work in cortically controlled neuroprosthetic systems has
concentrated on decoding natural behaviors from neural
activity, with the idea that if the behavior could be fully decoded
it could be duplicated using an artificial system. Initial estimates
from this approach suggested that a high-fidelity signal
comprised of many hundreds of neurons would be required to
control a neuroprosthetic system successfully. However,
recent studies are showing hints that these systems can be
controlled effectively using only a few tens of neurons.
Attempting to decode the pre-existing relationship between
neural activity and natural behavior is not nearly as important as
choosing a decoding scheme that can be more readily
deployed and trained to generate the desired actions of the
artificial system. These artificial systems need not resemble or
behave similarly to any natural biological system. Effective
matching of discrete and continuous neural command signals
to appropriately configured device functions will enable
effective control of both natural and abstract artificial systems
using compatible thought processes.
Addresses
1
The Biodesign Institute & Harrington Department of Bioengineering,
ECG 334, Arizona State University, Tempe, Arizona, 85287-9709, USA
e-mail: [email protected]
2
Department of Biomedical Engineering, Case Western Reserve
University, 10900 Euclid Avenue, Cleveland, Ohio, 44106, USA
3
Louis Stokes Cleveland VA Medical Center, 10701 East Boulevard,
Cleveland, OH 44106, USA
e-mail: [email protected]
Current Opinion in Neurobiology 2004, 14:758–762
This review comes from a themed issue on
Neurobiology of behaviour
Edited by Alexander Borst and Wolfram Schultz
Available online 5th November 2004
0959-4388/$ – see front matter
# 2004 Elsevier Ltd. All rights reserved.
DOI 10.1016/j.conb.2004.10.013
Abbreviations
FES functional electrical stimulation
Introduction
‘We will be engaged in the development of principles and
techniques by which information from the nervous system can
be used to control external devices such as prosthetic devices,
communications equipment, teleoperators [. . .] and ultimately
perhaps even computers’ [1].
Current Opinion in Neurobiology 2004, 14:758–762
Karl Frank, Founder of the Laboratory of Neural Control
at the NINDS, 1968.
In the above quote from 1968, Frank described the
mission of the newly created Laboratory of Neural
Control at the National Institutes of Health. This mission
is now coming to fruition, ironically enough, with the first
major milestones being in the realm of computer control
and, more recently, control of robotic arms. Recent
advances in recording and computer technology have
made it possible to utilize signals from large numbers
of neurons to drive external systems in real time. These
advances have captured the public imagination with
images of high-tech devices being controlled by one’s
thoughts. In this high-profile research environment, it
seems appropriate to step back and put this work into
perspective. Here, we discuss the progress that has been
made in the broad field of brain interfacing for device
control, and contrast the philosophies and successes
experienced by a range of approaches to the design of
control algorithms.
Why cortical control
As a result of advances in medical technology, more
people are now surviving severely paralyzing injuries or
diseases [2]. This has spawned an increase in the development of assistive technology to improve the quality of
life of these individuals. For people who are almost
completely paralyzed or ‘locked-in’, owing to amyotrophic lateral sclerosis or brainstem stroke, specialized
typing software can provide a way to communicate with
their caregivers and commercially available wheelchairmounted robotic arms can enable them to interact physically with the world. For people with high-level spinal
cord injuries, restoration of arm and hand function is now
possible through the use of implanted functional electrical stimulation (FES) systems that activate paralyzed
muscles through controlled stimulation of the peripheral
nerves [3,4]. By decoding movement intent directly from
the brain and then creating the desired movement
through FES, paralyzed individuals could, once again,
move their arms and hands just by thinking of doing so
[5,6].
Although restoring movement by thought has much conceptual appeal, the use of cortical signals has to be
weighed against the other command options available
to a person. For completely paralyzed individuals, brain
signals are their only option. For people with high-level
spinal cord injuries (C1–C4), command signals can still
be generated from the neck upwards. However, many
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Signal acquisition and analysis for cortical control of neuroprosthetics Tillery and Taylor 759
options, such as mouth-operated joysticks, tongue-touch
keypads and voice commands, interfere with talking,
eating and normal social interaction. Making useful
arm movements (FES-activated or robotic) requires multiple proportional command signals that are not always
easily generated for a given task from the face and neck.
Accessing desired movement directly from the brain
could replace, or at least augment, these less convenient
command options and improve the quality of life of
severely paralyzed individuals.
Volitional control of cortical neurons
Some have questioned whether or not we have volitional
control over the activity of our cortical neurons. The
entire field of motor cortical physiology is devoted to
studying those areas of the cortex which are ‘turned on’
when we want to move. Could there be a more direct
example of ‘volitional control’ over neuronal firing rates?
However, we are still learning the extent to which one has
direct access to the firing patterns of individual neurons.
Certainly, when we send out motor signals we do not
think about inducing a particular firing pattern; we think
of the desired result (e.g. pick up the glass) and, through
practice and experience, we produce the firing patterns
necessary to make the desired action take place.
It was not until the 1960s that explicit studies of an
animal’s ability to control the firing rates of individual
neurons began in earnest [7–14]. In these operant conditioning studies, animals were provided with feedback of
the firing rates of cortical neurons and/or rewarded for
changing these signals on command. Animals showed the
ability to raise or lower neural firing rates at will, alter the
normal relationship between firing rate and muscle activity, alter the firing relationship between two neighboring
neurons and put firing rates of individual neurons into a
sequence of specific ranges on command. Recent experiments in humans have shown a similar ability to regulate
the firing patterns of recorded cortical neurons [15,16] or
invasively recorded field potentials [17,18] at will. A
long history of electroencephalographic studies have
already shown that people can learn to modulate scalp
surface potentials as well [19].
Conversion algorithms
The aim of a neuroprosthetic system is to ‘read’ a person’s
intent from the recorded neuronal signals and use that
information to command another device. The form of the
command signal (i.e. discrete or continuous) depends on
the device one is aiming to control. To generate command signals for continuous systems, such as robotic arms
or computer cursors, the dominant approach has been to
record an ensemble signal during natural movements
(usually arm movements) and then develop a decoding
scheme that enables reconstruction of those movements
from the neuronal signals [20–24,25,26,27]. The core
assumption is that, if one can understand exactly what an
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animal is trying to do, then it becomes a matter of
engineering to accomplish the same task with another
device.
The analysis of ensemble neural activity during natural
arm movements has increased our fundamental understanding of how the nervous system controls movements
but it might not be the best approach for identifying
decoding algorithms to convert neural activity into device
commands. First, there is no reason to believe that the
neural activity associated with thoughts of moving an
external device, such as a computer cursor or a robot,
will be the same as that generated when thinking about
moving one’s own arm. Two studies that compared neural
activity during target-directed arm movements with
equivalent movements made using a brain-controlled
virtual cursor showed substantial differences in the directional tuning of the neurons between the two tasks
[24,26].
The different dynamics of an external system might also
require the neural activity to change when switching from
arm control to device control. The same two laboratories
have recently reported training monkeys to use intracortical signals for closed-loop control of a robotic arm in
three-dimensional space [25] or in two spatial dimensions plus gripper closure [26]. Both studies report that
the animals easily adjusted to the novel dynamics of the
robotic systems but, again, the neural activity changed
when the animals switched from normal arm movements
to the brain control tasks.
Another limitation of optimizing decoding algorithms on
neural activity recorded during natural movements is that
it does not take advantage of the inevitable learning that
takes place when a person or an animal has real-time
feedback of their brain-controlled movements. The operant conditioning work of the 1960s and 1970s clearly
demonstrated that animals could learn to alter their neural
output with practice when given real-time feedback of
their neural activity [7–14]. More recent studies in closedloop two- or three-dimensional continuous braincontrolled movement tasks have shown that monkeys
improve performance in these tasks with training
[24,26]. Recent human studies that used brain surface
signals to control a cursor demonstrated that performance
improvements can occur within minutes [18].
In the previously described monkey study, in which
animals controlled a three-dimensional cursor or robotic
arm, the strength of directional tuning of the individual
recorded units significantly improved with regular practice. Within just 12 training sessions, each with a duration
of about one hour, the average strength of directional
tuning of the recorded units during the brain control task
significantly exceeded the strength of directional tuning
exhibited during normal arm movements [24]. In further
Current Opinion in Neurobiology 2004, 14:758–762
760 Neurobiology of behaviour
offline studies, maximum likelihood analysis of neuronal
activity recorded during normal target-directed arm
movements showed that the intended target could only
be predicted with about 65% accuracy [28]. The small
ensemble of recorded neurons did not naturally convey
enough information about the intended target for accurate prediction. However, when the same animal made
similar target-directed cursor movements using its brain
signals directly, the visual feedback of the braincontrolled cursors enabled the animal to learn to modulate its recorded signals more effectively. A similar
maximum likelihood estimation of target location based
on neural activity recorded during the closed-loop braincontrolled movements improved prediction accuracy to
80%. With continued practice, the animal learned to
make long continuous sequences of three-dimensional
brain-controlled movements to 50–70 targets at a time,
without missing [24].
Neuron selection
Determining which neuronal ensembles will form the
best mapping between cortical activity and a specific
device command is still an unresolved issue. First, different brain regions might contain units that are more
appropriate for one neuroprosthetic task than another. For
example, recent work suggests that, although the motor
cortex might be useful for encoding continuous motion,
dorsal premotor cortex [29,30,31] or posterior parietal
cortex [32,33] might be able to provide a higher-level
estimate of the goal of a movement. However, the pattern
classification analysis employed in these recent studies is
best suited for selection among a limited set of discrete
goals as opposed to determining a reach goal from a
continuous range of possible goals throughout the workspace. The discrete pattern classification techniques
demonstrated thus far might be most effectively applied
in computer-based systems designed to enable severely
paralyzed individuals to choose efficiently between a
fixed set of different letters, words or icons distributed
around a computer screen. Many complex assistive
devices have been designed to convert discrete goal
requests into detailed plans for action (e.g. NavChair
[34,35]).
Continuous movement command signals can also be
effectively used in discrete choice selection tasks just
by moving the cursor to one of a fixed number of targets.
To speed up the goal-selection process, trajectories do not
need actually to reach the goal target. They only need to
proceed far enough toward the desired target for the
controller to determine which target is the intended goal.
Post hoc analysis of continuous brain-controlled cursor
movements in an eight-target three-dimensional centerout task has shown that discrete target selection could
have been accurately predicted just from the initial part of
the brain-controlled trajectories [25,28]. This is not
surprising, considering that studies of motor cortical areas
Current Opinion in Neurobiology 2004, 14:758–762
have shown that target direction is strongly encoded early
on in center-out reaching movements [36]. Training a
person to use their brain signals for continuous cursor
control might provide them with more flexibility, giving
them both rapid discrete choice selection and continuous
cursor control using the same learned brain-control skills.
Ensemble size
Another issue to address is how many neurons are really
needed for good long-term reliable control of various
neuroprosthetic systems. The number of neurons obtainable with the current hardware must meet or exceed the
number needed for adequate command before the benefits will outweigh the risks of such a device. Several
research laboratories have now shown good real-time twoand three-dimensional continuous or discrete control
using ensembles in the tens of neurons [22–24,25,
30,31,32,33]. However, there are suggestions that good
reliable long-term control will require as many as hundreds of neurons [26,29,37].
A couple of issues stand in the way of fully putting this
debate to rest. One issue is the stability of the recorded
units over time. Recording many units will provide a more
robust command source that can remain stable as neurons
drop out or enter the mix over time. It is likely that
recording stability will increase as better implantable
electrodes are designed [38–41].
Another unresolved issue is the extent to which neurons
will learn to produce the desired command signals with
continuous daily practice. The monkey studies to date
have only utilized the recorded neurons in brain control
tasks for a limited time each day. The animals then go
back to using those same neurons for normal motor
control activities for the rest of the time. It is likely that
the recorded neurons will become very proficient at
command of a given device over time, once their neural
activity is consistently used exclusively for that task. This
was seen to some extent in the three-dimensional brain
control experiment described above, in which the tuning
quality during brain control improved with regular daily
practice [24]. This neural plasticity might also be able to
compensate for shifts in the recorded populations over
time but only if brain interfacing and decoding systems
are appropriately designed to facilitate the transition and
retraining of changing neural populations.
Although being able to record a lot of neurons sounds
desirable, more is not always better. In one recent experiment, limiting the neurons used for decoding to only the
best units improved movement accuracy. Movement
quality deteriorated when additional lower-quality units
were included (R Wahnoun, J He, SI Helms Tillery,
unpublished). Selective use of the recorded neurons is
also important because the number of recorded signals
and the complexity of the conversion algorithm will affect
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Signal acquisition and analysis for cortical control of neuroprosthetics Tillery and Taylor 761
the amount of hardware needed to utilize the signals for
device control. For a mobile wheelchair user, the racks of
external equipment needed to apply complex sorting and
conversion algorithms to high-density data might not be
worthwhile if most of the command information can still
be obtained using a smaller subset of neurons and simple
decoding algorithms implemented on small portable
hardware.
To date, cortical activity has only been used to control,
at most, three simultaneous degrees of freedom. At first
glance, complex systems, such as paralyzed arms and
hands or multijoint robotic devices, appear to require
complex command signals, suggesting that large numbers
of recorded neurons will be needed for adequate control.
However, careful system design will minimize the number of command signals and, therefore, the number of
neurons required for any given device. For example, a
paralyzed arm, along with most multiaxis robotic systems,
can be directed using simple three-dimensional endpoint
commands. The device controller can transparently take
care of the complex details of determining muscle activations or joint torques needed to achieve the desired
endpoint path. Hand function, which at first glance
appears to be extremely complex with more than 20
degrees of freedom, can be adequately described using
just a few aggregate variables [42–44], and restoration of
practical hand function has been demonstrated with
current FES systems that utilize very simple command
signals [3–6].
Conclusions: moving forward
The combination of our current knowledge of motor
systems with the development of relatively cheap,
high-speed computing and multichannel recording systems has produced rapid advances in the field of cortically
controlled neuroprosthetics. Both human and animal
studies have now demonstrated that it is possible to
utilize neural signals in real time to control the motion
of a robotic arm or a computer cursor and to make discrete
choice selections. This technology holds great promise for
improving the quality of life of severely paralyzed individuals. However, the field is in something of an arms
race, with a push to record more neurons and implement
more complex algorithms to extract details about arm
movements from healthy behaving animals. This approach
has provided rapid advances in both neuroprosthetics and
neuroscience, but learning the strengths and limitations of
neural plasticity is essential for rapidly moving this field
forward. Future research must be directed toward practical
implementation in people with severe movement impairments in order to understand fully and utilize the potential
of the brain in the paralyzed population.
Update
Since this article was written, Cyberkinetics Neurotechnology Systems Inc. released their initial findings from
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the first subject enrolled in a pilot study to test their
product, the BrainGateTM neural interfacing system
(http://phx.corporate-ir.net/phoenix.zhtml?c=182802&p=
irol-newsArticle&ID=628347&highlight=). Although
human testing of intracortical signals for computer control
has been underway for several years (Kennedy et al.
[15,16], now with Neural Signals Inc.), the early studies
were limited to subjects who were almost completely
paralyzed. The new study by Cyberkinetics Neurotechnology Systems Inc marks the first use of chronic multichannel intracortical microelectrodes in a subject with a
high-level spinal cord injury. This first subject demonstrated that single and multiunit action potentials can be
recorded from the motor cortex three years after a spinal
cord injury and that the subject could volitionally modulate his recorded neural signals to move a computer
cursor in a controlled manner. Notably, the subject is able
to carry on conversations with ease while manipulating
the brain-controlled cursor. Although the accuracy of
cursor movements leaves room for improvement, the
quality of the cursor movements appears to exceed those
obtained by this group in previous monkey studies that
utilized similar hardware and software [23]. Over time,
results from this study will provide valuable information
on the effects of long-term training in humans. The influx
of venture capitol funding for this project played a substantial part in reaching this milestone, and reflects the
promise of continued progress in this field.
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