REVIEWpublished: 31 October 2017
doi: 10.3389/fnbot.2017.00059
Selectivity and Longevity of
Peripheral-Nerve and Machine
Interfaces: A Review
Usman Ghafoor 1 , Sohee Kim 2 and Keum-Shik Hong 1, 3*
1
School of Mechanical Engineering, Pusan National University, Busan, South Korea, 2 Department of Robotics Engineering,
Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea, 3 Department of Cogno-Mechatronics
Engineering, Pusan National University, Busan, South Korea
Edited by:
Florian Röhrbein,
Technische Universität München,
Germany
Reviewed by:
Vassiliy Tsytsarev,
University of Maryland, Baltimore,
United States
Joel C. Bornstein,
University of Melbourne, Australia
*Correspondence:
Keum-Shik Hong
kshong@pusan.ac.kr
Received: 22 April 2017
Accepted: 17 October 2017
Published: 31 October 2017
Citation:
Ghafoor U, Kim S and Hong K-S
(2017) Selectivity and Longevity of
Peripheral-Nerve and Machine
Interfaces: A Review.
Front. Neurorobot. 11:59.
doi: 10.3389/fnbot.2017.00059
For those individuals with upper-extremity amputation, a daily normal living activity is
no longer possible or it requires additional effort and time. With the aim of restoring
their sensory and motor functions, theoretical and technological investigations have been
carried out in the field of neuroprosthetic systems. For transmission of sensory feedback,
several interfacing modalities including indirect (non-invasive), direct-to-peripheral-nerve
(invasive), and cortical stimulation have been applied. Peripheral nerve interfaces
demonstrate an edge over the cortical interfaces due to the sensitivity in attaining cortical
brain signals. The peripheral nerve interfaces are highly dependent on interface designs
and are required to be biocompatible with the nerves to achieve prolonged stability and
longevity. Another criterion is the selection of nerves that allows minimal invasiveness
and damages as well as high selectivity for a large number of nerve fascicles. In this
paper, we review the nerve-machine interface modalities noted above with more focus on
peripheral nerve interfaces, which are responsible for provision of sensory feedback. The
invasive interfaces for recording and stimulation of electro-neurographic signals include
intra-fascicular, regenerative-type interfaces that provide multiple contact channels to
a group of axons inside the nerve and the extra-neural-cuff-type interfaces that enable
interaction with many axons around the periphery of the nerve. Section Current Prosthetic
Technology summarizes the advancements made to date in the field of neuroprosthetics
toward the achievement of a bidirectional nerve-machine interface with more focus on
sensory feedback. In the Discussion section, the authors propose a hybrid interface
technique for achieving better selectivity and long-term stability using the available nerve
interfacing techniques.
Keywords: peripheral-nerve, longevity, neuroprosthetics, selectivity, amputation, nerve-machine interfaces,
stability, electro-neurographic signals
INTRODUCTION
According to Ziegler-Graham et al. (2008), approximately 2 million people in the United States
have suffered from the loss of limb(s). Substantial progress has been made in the form of highly
sensorized cybernetic prostheses for restoration of the sensorimotor functionalities of limbs for
those who have undergone an amputation. Achieving an effective interface between the nervous
system and prostheses takes a long time due to the limitations of necessary components. The recent
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peripheral nerve interfaces: In those amputees who lost only
sensory end organs, the nerves connecting the end organs to the
brain are functioning normally.
The rapid development of bidirectional peripheral-nerve
interfaces has made available several methods for recording of
motor commands from nerves and/or stimulation on nerve
fibers. The main characteristics in bidirectional interfaces are the
selectivity of axons and the degree of invasiveness. Achieving
an optimal effectiveness entails a tradeoff between the reduced
invasiveness for stability and the sufficient invasiveness for
greater selectivity, in which the latter might damage the
nerve and also affect chronic stability. For applications, an
interface involving multiple electrodes implanted in an amputee’s
peripheral-nerve should be able to evoke a sensory response,
record motor commands, and control multiple limb functions.
Micera et al. (2008) suggested that the first step toward a suitable
reproduction of sensorimotor functionality is to determine
the topographical location in the nerve for desired functions.
According to Gustafson et al. (2009) and Badia et al. (2010),
the identification of fascicular contact points for electrode
positioning is absolutely essential to selective interfacing with
individual fascicles in a particular nerve. A various types
of devices to improve the nerve-machine interface for the
restoration of lost neural functions can be pursued. However,
the long-term stability and selectivity of neural recording and
stimulation remains a challenge. A key for an advanced neuroprosthetics is the achievement of selective sensory perception,
durability, and natural discernment from the nerve-machine
interface. The selectivity, longevity, and long-term stability are
absolutely an integral part for the establishment of long-lasting
and functional sensory feedback for an amputee (Warren et al.,
2016; Wurth et al., 2017).
The aim of the present review is to discuss the invasive and
non-invasive methods in detail for evoking sensory feedback and
recording intended motor commands from peripheral nerves.
The authors first briefly outline the structure of the peripheral
nerves that are responsible for transmitting motor commands
and sensory feedback signals from/to the brain. Second, three
different methodologies for evoking sensory feedback will be
discussed: (i) Indirect (non-invasive) elicitation feedback by
applying pressure or current, (ii) direct (invasive) elicitation
through the peripheral nerve using several types of interfaces, and
(iii) a cortical stimulation method to activate the somatosensory
cortex in the brain. Figure 2 breaks down the methods discussed
in this paper for providing sensory feedback to a bionic
prosthesis. We will also discuss two hybrid techniques for
bidirectional control of a bionic arm from the perspective
of selectivity and longevity. Finally, in section Discussion, we
propose a scheme for enhanced selectivity, longevity, and longterm stability for sensory feedback using the current techniques
available in the field.
research in prosthetic technologies reveals a trend toward natural
bidirectional communication between the amputee and the
bionic arm/prosthesis and subsequently possible solutions in
developing such a prosthesis that enables sensory restoration.
The ultimate goal is to develop a bidirectional interface between
the nervous system and a given prosthesis (e.g., a bionic hand,
arm, or leg). This can be achieved using the form of a closedloop control that sends motor commands on efferent pathways
and returns the sensory feedback on afferent pathways by means
of stimulation. According to several studies (Micera et al., 2008;
Rossini et al., 2010; Raspopovic et al., 2014), an ideal closedloop bidirectional prosthesis-user interface has the following
mandatory components (see Figure 1 below): (i) peripheralnerve data-recording electrodes; (ii) decoding of user intention;
(iii) production of motor commands for the prosthesis system;
(iv) passage of this information to the controller for controlling
the speed/force for handling an object; (v) sensors embedded in
the bionic hand/arm to capture the environmental information;
and (vi) a sensory subsystem to encode the feedback to an
amputee through a nerve stimulator (stimulation electrodes),
which evokes the sensation produced through a contact with
the manipulated object. Particularly for those who have lost
upper limbs, natural sensory feedback through the prostheses
is especially desired (Biddiss et al., 2007; Pylatiuk et al., 2007;
Wijk and Carlsson, 2015). Witteveen et al. (2012, 2015) found
that sensory-feedback systems improved the control of prosthetic
hands, eradicated the unpleasant phantom limb pain sensation
(Flor et al., 2001), and enhanced the sense of self-esteem.
Object discrimination tasks were successfully accomplished by
an amputee using artificial tactile and proprioceptive feedbacks
without visual or auditory cues (Horch et al., 2011).
Lost functions were restored to the patients with central
nervous system damages by electrically activating the intact
tissues distal to neural lesions. This kind of activation can be
realized via interfaces positioned either on the body surface
or on the muscles near the motor points, or can be directly
implanted in the motor nerves (Kuiken et al., 2004; Clemente
et al., 2016; Davis et al., 2016; Patel et al., 2016). Nerve-fiber
recording and stimulation from the nervous system have been
carried out with the help of electrodes (Leventhal and Durand,
2003). Over the past 70 years, peripheral nerve interfaces enabling
the restoration of sensorimotor functions have been improving
and evolving (Branner and Normann, 2000). Now, with these
systems, researchers are able to record the activity of the nerves
elicited by volitional movements and electrically stimulate the
peripheral nerves to move muscles. Direct nerve stimulation has
several advantages over alternative methods, which include the
low power consumption, multiple-muscle control via a single
implantation, and the inherent capability of positioning the
interface away from the contracting muscles (Koole et al., 1997).
Peripheral-nerve electrodes, to be effective in neuroprosthetic
applications, must have the capacity of targeting specific axons
for activation without stimulating others. Moreover, they must be
safe and biocompatible, and have stimulation characteristics that
will remain stable over many years. This review mainly focuses
on the amputees having limb loss(es) (not those having spinal
cord injuries), because the scope of this review is restricted to
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STRUCTURE OF PERIPHERAL NERVES
The roots of the peripheral nervous system lie within the spinal
cord, and the axons spread inside the peripheral nerves to reach
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FIGURE 1 | Concept of bi-directional control for bionic arm systems.
the target organs. In this way, the peripheral nervous system
fulfills its chief responsibility; the transmission of information
between the brain and the extremities. In the peripheral nerves,
numerous motor (called efferent) and sensory (called afferent)
fibers are present (Johnson et al., 2000; Fisher et al., 2014;
Mildren and Bent, 2016). The afferent fibers ranging 2–20 µm
in diameter, either myelinated or unmyelinated, terminate at
specialized sensory receptors in the skin, tissues and muscles.
These fibers are responsible for sending mechanical, thermal,
and noxious stimuli to the brain. Efferent motor fibers, on the
other hand, send movement/motor commands from the brain
to muscles. Both afferent and efferent nerve fibers are clustered
individually in the form of fascicles that are surrounded by
connective tissues in the peripheral nerves. In addition to the
bundles of nerve fibers, three supportive sheaths bind the fibers
in an organized structure (namely, epineurium surrounding the
nerve, perineurium surrounding the fascicle, and endoneurium
surrounding the fiber). Several researchers over the years have
developed interfaces for accessing multiple and distinct levels of
the nerves by penetrating the protective sheaths longitudinally
and/or transversally. In this review, we will focus more on
afferent fiber interface techniques for provision of sensory
feedback.
to Johansson and Vallbo (1979), the most innervated parts
in the body are hands, which permit fine manipulation and
precise perception of the environment. As an illustration of
a human finger’s utility, the fingertip has approximately 241
units/cm2 of cutaneous mechanoreceptors for very fine sensory
resolution, as compared with the palm that has 58 units/cm2
only. The important sensory feedbacks required for prostheses
are proprioception and tactile sensation. For instance, cutaneous
mechanoreceptors are essential for executing daily routines and
simple tasks such as the grasping a glass. Johnson et al. (2000)
and Johansson and Flanagan (2009) have demonstrated that
detection of minute slippage of an object from a hand is by
Meissner corpuscles and rapidly adapting afferents; to avoid
slippage, a reflexive force is triggered. This type of testing,
therefore, might be useful for assessment of the complete
functionality of a rapidly adapting system. In the past, several
approaches have been used to test and provide sensory feedback
to bionic arms/prostheses. In the next section, non-invasive
methods utilized for indirect sensory feedback are discussed.
Afferent Receptors
Several substitutions for sensory perception have been developed,
which do not require implantable interfaces (Lundborg and
Rosen, 2001; Visell, 2009; Khasnobish et al., 2016): Sensory
substitution is a technique to provide an alternative path for
necessary sensory information to the body using other sensory
passages that are different from those naturally used. For
instance, Kaczmarek et al. (1991) revealed that hearing and
vibration can serve as a substitute for touch and pressure,
respectively. Due to the essential need to restore physiological
sensory information for those who have gone through a
traumatic event and/or amputation, many of these sensory
substitutions have been employed for battery-powered prostheses
NON-INVASIVE METHODS OF SENSORY
FEEDBACK
Touch, pressure, proprioception, temperature, and pain
fall under the rubric of somatic sensation. The somatic
receptors are divided according to the following broad
categories: (i) proprioceptors that provide information on
joint position and motion; (ii) nociceptors for temperature,
pressure, different chemical stimuli, and a combination
of these; (iii) thermoreceptors for mild temperature; (iv)
cutaneous mechanoreceptors for touch and pressure, and (v)
chemoreceptors for detection of certain chemical stimuli. Several
types of sensory receptors are available in the body, and their
quantity varies with respect to their body location. According
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on the stimulation voltage or current. Although electro-tactile
feedback cannot replicate direct forces and special types of touch
sensations yet, it can produce a wide range of tactile sensations.
Also, electro-tactile stimulation has been used for providing an
alternative way to visual perception to the blind (Kaczmarek et al.,
1991). The experimental study with electro-tactile feedback by
Perovic et al. (2013) showed its suitability for haptic perception
as well. Their results revealed that electro-tactile feedback has a
potential for delivering haptic sensations from devices such as
prosthetic hands. The users of this modality have been able to
regulate the grasping forces as well as to the angular displacement
of the prosthetic hand to predefined levels (Clemente et al., 2016;
Schweisfurth et al., 2016). Further, Patel et al. (2016) improved
the control performance by the grasping function by providing
tactile stimulations to individual fingers (multiple degrees of
freedom) in comparison to open-loop controls (Jorgovanovic
et al., 2014; Dosen et al., 2015; Isakovic et al., 2016; Xu
et al., 2016). Brain-computer interface (BCI) is a method of
communication between brain and hardware by means of signals
generated from the brain without the involvement of muscles and
peripheral nerves (Naseer and Hong, 2013, 2015; Naseer et al.,
2016; Rutkowski, 2016). An opposite modality of it is known as
BrainPort, which has been designed to support a direct link from
a computerized environment to the human brain non-invasively
(Danilov and Tyler, 2005). In the application of electro-tactile
stimulation (Tyler et al., 2003), the BCI and a head-mounted
accelerometer could be served as a vestibular substitution that
uses the unique patterns of electrotactile stimulation on the
tongue for restoration of head-body postural coordination. Most
types of electro-tactile stimulation are percutaneous (direct
stimulation of the nerves) and transcutaneous (through the
skin) stimulations. A major disadvantage of the percutaneous
type of electro-tactile stimulation is the additional distress or
pain to the patient. High voltage stimulation is required in
stimulation through the skin without any insertion leads because
of high impedance of the dry skin. This can be considered as
a disadvantage of the transcutaneous electro-tactile stimulation.
However the advantage of these types of feedback is the
requirement of relatively simple circuits and electrodes placed
on the skin, which can considerably reduce both the cost and
amount of hardware needed to deliver tactile sensations to a user.
FIGURE 2 | Breaks down of sensory feedback methods used in bionic arm
systems.
to achieve sensation. However, these modalities are not yet robust
or effective enough to be applicable to routine tasks of daily
living. Their use in commercially available prosthetics has not
yet been widely adopted; however, they have been successful in
controlled environments with certain limitations. The commonly
used techniques are electro-tactile and vibro-tactile sensory
substitutions as well as modality-matched feedback, which use
an electric current and mechanical vibration in the residual skin
area of the limb. This can help in encoding information on object
manipulation, grasping force, elbow angle, and approaching
direction (Hsiao et al., 2011; Chen et al., 2016; Clemente et al.,
2016; Isakovic et al., 2016; Xu et al., 2016).
Electro-Tactile Stimulation
The electro-tactile sensory modality is a method of passing
electrical current through the skin of an amputee to elicit
perception (Yem and Kajimoto, 2017). The sensation is not
necessarily confined to the zone under the stimulating device,
and the elicited sensations can spread if these are placed near the
nerve bundles. Electro-tactile (electrocutaneous) stimulation is
either voltage- or current-regulated. The use of voltage-regulated
stimulation can minimize the chance of skin burns. Visell (2009)
showed that the change in impedance and load at the site of
interface might not affect the value of current in electro-tactilecurrent-regulated stimulation. Multiple features of this modality
can be controlled to elicit sensory percepts. These features
include (i) the place, material, and geometrical properties of the
interface, (ii) the parameters of the current (duration, frequency,
and amplitude), and (iii) end-organ thickness and location of
the skin stimulated (Boldt et al., 2014; Hartmann et al., 2015;
Paredes et al., 2015; Strbac et al., 2016). Subjects often describe
electro-tactile sensations, qualitatively, as burning, pain, touch,
pinch, tingle, pressure, vibrations, itch, sharp, etc. depending
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Vibro-Tactile Stimulation
Percept sensation in the residual limb also can be generated
using vibro-tactile stimulation elicited by mechanical vibrations
of the skin (Tanaka et al., 2015). Different stimulation parameters
(i.e., frequency and amplitude of the vibration) result in different
types of sensory information such as proprioception (Kaczmarek
et al., 1991; Mildren and Bent, 2016). However, other parameters
such as duty cycle, shape, and pulse duration also can be used
to convey different types of feedback (Cipriani et al., 2012;
Dosen et al., 2016). The discriminating values of amplitude
thresholds differ by prostheses and interface locations. Several
sensory substitutions have been achieved through vibro-tactile
stimulation, which includes the mapping of sensation from the
prosthetic hand to the phantom hand for transradial amputees
(Antfolk et al., 2013b; Papetti et al., 2016), the increase and
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Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces
section, we will review the progress made in the neuroprosthetics
using implantable interfaces at neuraxis and we will explore
peripheral nerve-machine interfaces used for recording and
stimulation.
decrease of texture roughness feeling on fingers (Asano et al.,
2015), the sensation through stretching of the skin (Wheeler et al.,
2010; Motamedi et al., 2017), the improvement in grasping force
(Chatterjee et al., 2008; Cipriani et al., 2008, 2014; Saunders and
Vijayakumar, 2011; Montagnani et al., 2015; Witteveen et al.,
2015), the evoked sense of proprioception by the vibrations
of a tendon (Thyrion and Roll, 2010), an object manipulation
(Rombokas et al., 2013), the sense of embodiment (D’Alonzo
et al., 2015; Ko et al., 2015), and the sensation on the surface
of the skin (Kangas et al., 2017). The early devices were heavy
and energy consuming. However, with the help of sophisticated
electronics, tactile vibrators are now low powered and easily
used for prosthetic applications. Generally, the use of vibrotactile
feedback improves the user performance through a better control
of grip forces and by lowering the number of errors in task
execution. Unfortunately, due to the lack of intuitiveness and
usability, the users don’t feel comfortable with these types of
indirect feedback systems.
INVASIVE METHODS OF SENSORY
FEEDBACK
In the indirect methods noted above, complete selectivity
and longevity have not yet been achieved. These modalities
are still unable to provide selective sensory information in a
stable manner. To overcome this issue, the peripheral interface
technology has advanced more in recent years, and researchers
are now focusing more on invasive technologies (Navarro et al.,
1998, 2005). Tyler and Durand (1997) focused on the fact that
for those amputees who only lost sensory end organs (limbs), the
peripheral nerves connecting the brain to the lost organs retain
the normal functionality. Thus, through a synthetic activation
of these pathways, perception of sensation can be achieved
(Micera and Navarro, 2009). There are several locations at which
the interaction with the residual somatosensory system can be
established (Weber et al., 2012). Direct stimulation on the nerve
has earned extensive popularity in providing sensory feedback
to prostheses. Preclinical works on the approaches related to
the brain (Bensmaia and Miller, 2014), dorsal root ganglion
(Weber et al., 2006, 2011; Bruns et al., 2013), and intra-spinal
microstimulation (Gaunt et al., 2006; Capogrosso et al., 2016) are
progressing.
Modality-Matched Feedback
The most recent non-invasive technique used for conveying
sensory information is the modality-matched feedback (see the
mechano-tactile stimulation below): The input sensory stimulus
must be the same modality as that of the sensory output. For
example, for the sensation produced by touching, the prosthesis
is required to be perceived as touch. This methodology is closer
to naturally generated percepts. In theory, the coupling of noninvasive electro-mechanical devices with thermoelectric devices
(e.g., Peltier cells) has made it possible to regain modalitymatched touch sensations including contact, vibration, texture,
temperature as well as normal and shear force/pressure. These
coupled devices can be applied on the skin (Davalli et al., 2000),
to the residual limb (Antfolk et al., 2012; Bjorkman et al., 2016),
and to the chest and other body parts (Panarese et al., 2009) for
achieving modality-matched touch sensations. This technique as
applied for restoration of proprioception is inherently a challenge
for engineers and researchers, as the angle of the wrist or hand
joints is required to be manipulated to another intact joint in
order to match the modality.
Mechano-tactile stimulation is utilized as one of the modalitymatched techniques, which uses the application of force on
a residual limb to evoke sensory feedback. An increase of
the force in the prosthesis brings a proportional increase
of the force applied to the skin in the residual limb. This
technique may be superior to vibro-tactile substitution in that it
reduces the grasping force error of prostheses, thereby providing
more accurate spatial sensory information (Patterson and Katz,
1992; Antfolk et al., 2013a). However, Antfolk et al. (2013b)
explained that the inherent design constraints on the size, weight,
and response time of an actuator reduce the impact of this
approach. D’Alonzo et al. (2014a,b) used hybrid vibro-electrotactile stimulation and found it to be an efficient approach for
obtaining multi-channel sensory feedback.
Regarding all the modalities noted above, achieving a selective
and stable interface remains a challenge. Hence, researchers have
looked for other near-to-natural-sensation modalities, among
which peripheral implants are popular these days. In the next
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Neural Interface and Advantages
In the twenty first century, two important developments have
transformed the field of neuroprosthetics. The first is the
improvement in bionic arms (or prosthetic limbs) that can
replicate the functions of a natural human arm. The second is the
enhancement of algorithms that decode the intended movements
of an amputee from neuronal activities in the motor cortex
area of the brain. By combining these innovations, it is now
possible for a human patient to perform tasks with a bionic
arm by thoughts alone (Collinger et al., 2013; Wodlinger et al.,
2015). Most existing interfaces are either from the nerve or the
cortex (Tabot et al., 2015). The restoration of somatosensation,
either from cortical stimulation or peripheral-nerve stimulation,
is required for effective bidirectional communication and a sense
of feeling or embodiment (Dornfeld et al., 2016) for the patient.
The essential need for touch in everyday life has led several
research groups to develop different techniques for its artificial
restoration. The necessary methodology entails stimulation of
the peripheral nerve or a somatosensory area of the brain
(S1) with trains of electrical pulses to evoke percepts that
transmit information from the grasping object (Downey et al.,
2016). Amputees or patients that use a bionic limb controlled
through such a bidirectional peripheral-nerve-interface perceive
the prosthesis as an integral part of themselves rather than
as a piece of hardware attached to their arm (Marasco et al.,
2011; Limerick et al., 2014; D’Alonzo et al., 2015). The dexterity
of a bionic arm can be improved through the restoration of
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and thus are less invasive and minimize the disturbances to the
neural tissue. The cuff interface is most common among the
extra-fascicular types. This electrode configuration can provide
several distinct stimulation/recording according to the contacts
around the periphery of the nerve. The helical (Agnew et al.,
1989) and spiral (Naples et al., 1988) type interfaces have been
proven to be stable for decades in clinical applications (Fisher
et al., 2009; Polasek et al., 2009). Their circular shape offers a
disadvantage of minimal interaction with neural tissues. Tyler
and Durand (2002) introduced the Flat Interface Nerve Electrode
(FINE), which is another form of extra-neural interface that
was developed to increase the surface area without penetrating
inside the nerve and to maintain the naturalistic shape of
the nerve. Its recording ability (Yoo and Durand, 2005) and
selectivity of the periphery of the stimulated nerve have been
demonstrated (Schiefer et al., 2013; Ortiz-Catalan et al., 2014).
Since the electrodes and the nerve fibers are separated by
the perineurium, higher stimulation currents are required to
achieve sensations than the case of intra-fascicular interfaces
(Grinberg et al., 2008). Leventhal and Durand (2003) have
employed a higher value of current resulting in activation at
subfascicle level and achieved a low selectivity, and also their
results were not repeatable: Hence, a similar tactile perception,
which is not a naturalistic pattern of neuronal activation, was
evoked. During grasping with an intact hand, every afferent
fiber perceived differently and responded as per the object’
features that invaded the fiber’s receptive field, while electrical
stimulation through the electrode sometimes produced a highly
unnatural feeling or paresthesia due to synchronous activation
of a large population of axons. The temporal modulation of
stimulation pulse trains somewhat mitigated the effect of tingling
and paresthesia. A stable and selective configuration of FINE
and spiral interfaces has been achieved in clinical trials for
more than 3 years for individuals with limb loss (Tan et al.,
2014, 2015). Selectivity also has been achieved in this way,
but only on the periphery of the nerve, not up to the axonal
level. The main drawback of extra-neural interfaces is their
low selectivity. In fact, being wrapped around the nerve, the
electrode records the whole electrical activity of the nerve.
As noted above, in order to reach afferent axons from the
periphery of a nerve, they must provide high stimulation currents
as compared to the intra-neural ones. For these reasons, an
intra-fascicular type that penetrates the nerve itself has been
introduced.
somatosensation by stimulating the peripheral nerve, because in
some cases such a manipulation of an object, visual feedback is
considered to be a poor substitute (Bensmaia, 2015). To obtain
such a feedback, several peripheral-nerve-interface approaches
have been devised over the past two decades.
As noted above, electrical interfaces can be established
anywhere in the cortex of the brain to the end organ. Sometimes
interfaces are penetrative and sometimes stimulation can be
given externally. In a broad spectrum, evaluation of these
interfaces can be based on selectivity and longevity: Longevity
is the measure of stable interaction of the electrodes with the
same population of sensory afferents over time, and selectivity
is the measure of its interaction with specific parts of sensory
afferents. For the evaluation purpose, computational models of
tactile afferents also have been deployed, which can simulate
a population of afferents, in real time, in milliseconds and
with precision (Kim et al., 2016). Furthermore, stability can
be defined as the duration that the information (related
with the activity measured by the electrode) should remain
constant over the life of the interface. Greater stability of a
stimulating electrode results in a more natural feeling of an
artificial touch and is also important in achieving longevity
for sensory feedback. Meanwhile, a higher selectivity can
be achieved through intra-neural implants and, along with
that, stability over a longer period also can be achieved. To
increase selectivity, penetrating array type interfaces are used.
However, extra-neural implants might stimulate a population of
axons.
An interface with a cortical region of the brain faces
similar longevity/stability and selectivity challenges, but in some
different manner from the nerve case. The fundamental problem
in cortical interfaces is longevity (Warren et al., 2016): The
neurons (or neuronal tissues) at the implanted surface and the
electrodes (implanted interface) degrade over time (McCreery
et al., 2010; Prasad et al., 2012; Kane et al., 2013; Chen et al., 2014).
These changes can affect the stimulation and recording abilities
of the electrodes for sensory feedback or decoding the attempted
movements (Perge et al., 2013).
Peripheral nerve stimulation through different interfaces is
the hot issue for restoring sensations. In the next section, we
will explain the recent advances for peripheral-nerve interfaces.
The functional properties, selectivity, and biocompatibility will
be discussed along with the advantages and disadvantages of
various peripheral-nerve interfaces. The techniques in wide use
for restoration of sensory feedback through electrical stimulation
will be highlighted, though some other potential stimulation
techniques such as the optogenetic modality (targeted neural
signaling) (Towne et al., 2013; Warden et al., 2014; Pisanello et al.,
2016) and the infrared-light-based technique (Wells et al., 2007;
Cayce et al., 2015) are excluded.
Intra-Fascicular Interfaces
As the name suggests, an intra-neural interface penetrates the
protective sheaths. The least invasive designs such as the groove
interface (Koole et al., 1997) and the slowly penetrating interfascicular nerve interface (Tyler and Durand, 1997) penetrate
only the epineurium. These interfaces physically insert electrical
contacts within the nerve. Afferent/efferent fibers are in reach
of these types of electrodes, and recording/stimulation is not as
difficult to achieve as in case of other modalities. These interfaces,
unlike extra-neural ones, tend to have more contacts within the
peripheral nerve. Among the most invasive penetrating intraneural interfaces placed inside the nerve fascicles, there are
Extra-Neural Interfaces
In general, two broad categories of peripheral-nerve-interfaces
are extra-neural and intra-neural. The extra-neural (or extrafascicular) interface with a circular shape that surrounds the
peripheral-nerve is a noninvasive method to the nerve itself. The
electrodes do not penetrate the protective sheath (perineurium),
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Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces
Regenerative Peripheral-Nerve Interface
(RPNI)
the Longitudinal Intra-Fascicular Electrode (LIFE) (Yoshida and
Horch, 1993; Dhillon et al., 2004; Thota et al., 2015), a conducting
wire or polymer filament implanted longitudinally and laid
parallel to the nerve fibers, and the Transverse Intra-fascicular
Multi-channel Electrode (TIME) (Boretius et al., 2010) having
aligned contacts perpendicular to the nerve, respectively. LIFE
has some drawbacks that include (i) a fixed distance between
the electrode and the nerve which limits its selectivity, (ii) the
interface stiffness causes micro-movements that, in the long
term, may damage the nerve. The thin film Longitudinal IntraFascicular Electrode (tfLIFE) is an upgraded form of LIFE. It
has been fabricated on a micro-patterned polyimide substrate,
allowing several contacts in one interface. Its flexible structure
allows a better adaptation to the nerve shape preventing damage
due to an excessive stiffness. Selective interfacing is possible with
LIFE and tfLIFE, which acquire signals from a small number
of axons (Kundu et al., 2014). They are less invasive than
recent multichannel array type interfaces. Selective stimulation
and recording have been achieved through TIME at both intrafascicular and inter-fascicular levels (Badia et al., 2011a, 2016).
Also, multi-electrode-array-based interfaces (Byun et al., 2017)
have been developed recently to demonstrate its viability for
achieving sensorimotor information from the peripheral nerves.
These interfaces have great benefits for individuals who have
suffered a limb loss. Moreover, another penetrating type of
interface is the Utah Slanted Electrode Array (USEA). This
type has varying electrode densities according to several designs
proposed in the literature with multiple penetration depths
(Branner et al., 2001; Ledbetter et al., 2013; Wark et al.,
2013). They are inserted perpendicularly into the peripheral
nerve leading to a higher risk of nerve damage. They require
a lower current for activation of nerve fibers due to their
close proximity to axons and fibers; significantly therefore, a
small group of fibers can selectively be stimulated (Branner
et al., 2004). In this chronic implantation, the stability of
an electrode of 80% silicon has been demonstrated for 7months (Lacour et al., 2016). Davis et al. (2016) have achieved
an increased selectivity and a stable sensory feedback by
chronically implanted USEA in the median and ulnar nerves
of human, which leads to an intuitive and dexterous control
of prosthetic fingers with sensory feedback in the future for
bidirectional prosthetic control. Coordinated grasp and sensory
responses by stimulating the peripheral nerves of a monkey
was demonstrated with the implantation of USEA. Moreover,
with short-term implantation of intra-fascicular electrodes,
increasing stimulation thresholds have been observed (Boretius
et al., 2010; Rossini et al., 2010). The reported number of
sensory perceptions and the locations evoked by intra-neural
interfaces are very similar to those of the long-term extraneural approaches, which led Grill et al. (2009) to conclude that
the two approaches are equivalent. One of their disadvantages
is the tendency to damage the nerve by penetration, which
reduces the long-term stability. Nerve stimulation and excitation
generated in the motor fibers can cause contraction of residual
muscles, which may result in an obstructed control of bionic
arms/prostheses.
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RPNI consists of an electrode and a residual peripheral nerve,
which is neurotized by transacting the nerve and inserting the
electrode in between them; it is an internal interface for signal
transmission with the external electronics of a prosthetic limb.
Effectively, it is a sieve made of silicon, ceramic or polymer
with a large number of fine holes that is placed inside the
transected nerve. The increase in growth rate of regeneration
of individual or a group of fibers through those holes can
activate selective stimulation and make it possible to record
action potentials (Micera and Navarro, 2009; Sando et al., 2016)
from individual axons or a small group. In principle, a high
number of selective contacts can be achieved by reducing the
size of the sieve along with an increased number of fine holes
(Lago et al., 2007a). The neurotized interface serves as a biological
amplifier and provides a long-lasting site for implantation.
Studies revealed that the RPNI undergoes robust regeneration,
neurotization, and revascularization (Urbanchek et al., 2016),
and that signals are reliably transduced across the interface with
longevity and reproducibility. Polyimide-based sieve electrodes
have been shown to be biocompatible (Stieglitz et al., 1997) and
stable over several months for in vivo implantation and testing
(Navarro et al., 1998). Further experimentation with the interface
revealed the stability and formation of new neuromuscular
junctions within the muscles for improved function in amputees.
Kung et al. (2014) proposed a new interface strategy to harness
motor commands from transected peripheral nerves for control
of a prosthesis. Also they demonstrated the 7-month viability of
the RPNI. On the other hand, stimulation of a small number
of regenerated fibers was shown feasible using the regenerative
electrodes. By matching the regenerated nerve fascicles with the
original receptive fields, an adequate feedback might be delivered.
By targeting the fiber growth for a specific feedback of perception
from a distinct neural population, there might be a possibility of
generating a different fiber population that might not be required
for the receptive field (Lotfi et al., 2011). Sensory feedback has
been achieved from tactile and force sensors embedded in the
prosthesis by stimulation of appropriate afferent fibers in the
transected nerves. Another methodology to attain feedback has
been proposed by Nghiem et al. (2015), which can be used
for transferring sensory signals from a prosthetic sensor to the
residual nerve. In this method, an insulated electrode placed on
the surface of a muscle was used to stimulate the muscle, which
then depolarized the afferent nerve within the muscle to provide
sensory feedback. This new design can eliminate the problem
of direct nerve stimulation that is inherent in intra-neural
interfaces and also limits potential peripheral nerve damage. This
sensory RPNI has the ability to transduce distinguishable and
graded sensory signals across the peripheral nerve when being
stimulated electrically. Although RPNI is still in an early stage of
development, this technique has the capability to access sensory
pathways and provide stable sensory feedback. A disadvantage
of this modality is that the functional use of electrodes might
require several months due to nerve regeneration. Furthermore,
the nerve might degenerate with time, which will lead to a
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Ghafoor et al.
Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces
substantial loss of stimulation ability of the implanted electrode
(Lago et al., 2005). A number of useful results have been obtained
using the RPNI technique in experimental models (Ceballos et al.,
2002; Ursu et al., 2016). However, some challenges remain, which
hinder its clinical usability. The most important breakthrough
of regenerative electrodes will be their implantation in the
peripheral nerves of an amputee for bidirectional interfaces.
TARGETED REINNERVATION (TR)
TR is a nerve-machine interface that has been developed to make
prosthetic control and feedback more intuitive. This method is
considered to be fully neither invasive nor non-invasive; it lies in
between due to the surgical requirement for its implementation.
It has demonstrated a success in improving the motor control
signals for both transhumeral and shoulder disarticulation
amputation (Dumanian et al., 2009). It has also shown promising
to sensory outcomes by using rerouted residual median, radial,
and ulnar nerves. Instead of a direct electrical interface with the
residual nerves in the arm, those in the arm can be moved to
reinnervate the intact muscles in the chest, see Figure 3. This can
help the transmission of sensory feedback and the attainment of
electromyographic (EMG) signals by surface electrodes from the
reinnervated site to control the prosthetic limb (Kung et al., 2013;
Cheesborough et al., 2015). The EMG signals corresponding
to the intended movement of a patient are generated from
muscle contractions of redirected nerves. When the target skin
of these patients was touched, they felt as if their missing limb
was being touched (Kuiken et al., 2007a; Marasco and Kuiken,
2010). Studies of two amputees also have demonstrated a touch
perception that aroused in the target skin: The amputees had a
strong impression that the sensations arising from stimulation of
the reinnervated skin site were projected to their missing limb.
Furthermore, the sensory afferents remained stable for years
after surgery. This methodology is being expanded in the field,
evoking cutaneous or tactile sensations (Hebert et al., 2016) from
the skin of reinnervated muscles. Using TR, sensation in the
hand as evoked by a reinnervated chest skin along with other
senses like touch, temperature, pain, and a limited graded force
discrimination have been demonstrated (Kuiken et al., 2007b;
Marasco et al., 2009, 2011; Schultz et al., 2009; Sensinger et al.,
2009). There is a possibility of generating tactile feedback, but this
technique is not naturalistic in restoring proprioception.
The advantages of TR technique, as stated by Hebert et al.
(2014a,b), are as follows: (i) A long-term stable interface is
possible, (ii) after rerouting of the nerves, there is no additional
surgical procedure, (iii) the body is free of implanted interfaces,
(iv) electrical stimulation evokes sensation to the reinnervated
skin patch, and (v) there is no paresthesia or tingling. A series of
results for patients who have undergone TR are; (i) non-invasive
stimulation at the innervated site resulting in a generation of
perceived sensory information (a cutaneous sensation) in the
median nerve of the hand, (ii) effective detection of touch,
pain, temperature, and proprioception to some extent, and (iii)
stable reinnervated area for the detection of graded forces. TR
does not only help in providing sensory feedback but also
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FIGURE 3 | Targeted reinnervation: Stimulation site on the chest for sensory
feedback.
increases the size of the sites that can be used to control the
prosthetic hand (Kuiken et al., 2004). However, Kristeva-Feige
et al. (1996) have explained that there is a difficulty in restoring
both sensory and motor control simultaneously because sensoryafferent signals are suppressed by motor-efferent commands. The
proximity of the sites of sensory feedback and motor control can
be considered as the major disadvantage of the TR technique.
Also, the tactor array used for elicitation of sensory feedback at
the TR location has only a limited ability in producing a wide
range of sensation. The required calibration routine for taking
the tactor array on and off has made this option rather difficult for
daily use.
BIOCOMPATIBILITY AND NEURAL
RECORDING/STIMULATION
Long-term stability and selectivity can be obtained if implanted
interfaces are bio-integrated (or biocompatible) with the
peripheral nerve. Foreign body reaction developed around the
implants can affect the signal to noise ratio of the recorded
motor commands and change the stimulation threshold values
for sensory feedback. Therefore, existing interfaces and their
fabrication have been considered as a practical choice for
a prosthesis that intends to the recovery of sensorimotor
abilities with characterization of long-term usability and
biocompatibility. Such interfaces have offered a high-resolution
means to access the information from the peripheral nerve by
processing the multi-unit recordings and stimulations. Electrical
stimulation of a single or a micro-electrode array has required
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Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces
of stimulation, which was due to the foreign-body response of
tissue fibrosis (Rossini et al., 2010).
Additionally, some cortical implants like the Utah Electrode
Array and the customized multi-channel stimulator for a
cortical visual neuroprosthesis can be used for blinds (Ferrandez
et al., 2007). Such stimulators have been proven to inject
current (charge) in a safe and precise way in an acute animal
experimentation. Another implant is the planar multi-electrode
array for recording from a rat auditory cortex and visualizing a
spatiotemporal structure of the cortical activities (Tsytsarev et al.,
2006). The key to a further development of neuroprosthetics is
to record simultaneous single-unit, multi-unit, and local field
potential activity from multiple brain sites. Most recent study by
Pothof et al. (2016) described the fabrication process of a chronic
neural probe that has similar material and geometrical properties
with that of clinical probes that enable recording of a single
neuron activity at a high spatial resolution. These probes have
successfully recorded single unit activities up to 26 days from
the brain of a monkey that suggests its potential usefulness on
human applications. Guitchounts et al. (2013) demonstrated the
viability of long-term recording using a carbon-fiber electrode
array, which have provided stable multi-unit recordings over a
time-scale of months.
It is evident from above discussions that more research
into long-term interfacing implants for bidirectional control of
prostheses is required. In the following section, some parameters
that can help in selective neural stimulation for elicitation of
distinct and graded sensations (sensory feedback) are discussed.
current pulsations for eliciting action potentials. However, this
approach is required to fulfill some conditions; (i) specifically
designed interfaces for peripheral nerves, (ii) invasive enough
to reach a target axon, (iii) ability to communicating bidirectionally, (iv) selective and stable electrical interfacing, (v)
minimal tissue damages and influence of foreign body responses,
and (vi) most importantly a biocompatible and biostable
interface. Several types of interfaces proposed in the literature
showed different material and chemical properties. Out of which,
polymer based neural interface is popular. A conducting polymer
is often used as a coating material on electrodes to increase
charge-injection capacity for neural stimulation and to get high
signal-to-noise ratio (SNR) for neural recording. Polypyrrole
(PPy) and poly (3,4-ethylenedioxythiophene, PEDOT) are the
most widely used polymer coatings for neural electrodes due
to their biocompatibility. Different types of interfaces have
been tested by several research groups on animals for checking
their biocompatibility, recording and stimulating abilities for
instance; Polyimide-based intra-neural implants (Wurth et al.,
2017), USEA (Christensen et al., 2014), tfLIFE and tripolar
cuff (Qiao et al., 2016), stretchable polymeric multi-electrode
array (Guo et al., 2014), TIME-2 and TIME-3 with different
chemical properties (Badia et al., 2011b), LIFE (Zheng et al.,
2008), polyimide, platinum intra-fascicular electrodes (Lago
et al., 2007b), and polymer-based longitudinal intra-fascicular
electrodes polyLIFEs (Malmstrom et al., 1998; Lawrence et al.,
2002).
As noted in the previous sections, several research groups
have tested the capacities of intra- and extra-fascicular interfaces
by implantation in the ulnar and median nerves to selectively
evoke sensation in amputees through multiple electrodes.
LIFE, TIME, FINE and the most recently used USEA have
demonstrated interfacing capability with peripheral-nerves for
sensory feedback. The details of the interface configurations
and stimulation parameters in each human study are tabulated
in Table 1. The artificial sensory signals are transmitted to
the peripheral nerves by implanting electrodes providing a
stimulated current that is proportional to the original input. By
means of real-time sensory feedback, the amputee is able to move
a prosthetic arm, apply a grip force via the recording of fine motor
commands, and also feel a sensation without any audio and visual
aids. Some studies have shown such ability of an amputee to
differentiate objects according to their perceived characteristics
(e.g., size, shape, and stiffness) and execute motor outputs such
as grip strength (Horch et al., 2011; Raspopovic et al., 2014).
The ability of object recognition and simultaneous encoding
of sensory information for manipulation of grip forces are the
excitatory developments for amputees. However, its widespread
applicability is reduced by the limited number of studies and
participants to date. Given the limited long-term data, it is
significant to consider the physical conditions of the electrodes
implanted in the peripheral nerves along with their capacity
to provide a long-term stable interface (Yoshida et al., 2010).
Structural changes of nerves can occur due to implantation.
Fibrosis can hinder the response of an electrode and constantly
reduce its performance. Implantation of tf-LIFE 4 in humans has
shown a complete termination of sensory detection after 10 days
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CHARACTERISTICS OF STIMULATION
Spikes are responsible for conveying information in the axons of a
nerve, and the spike rate or frequency is involved in transmitting
sensorimotor commands in a single axon (Tyler, 2015). Electrical
stimulation in the form of spikes has been given to the residual
stump nerve of a patient through typically implanted electrodes,
and psychophysical judgments and verbal reports were gathered
(Polasek et al., 2009; Schiefer et al., 2016). Stimulation through
different electrodes is one way of manipulating evoked sensory
percepts. Different populations of afferents can be activated
through these individual electrodes, which can have distinct
receptive field locations (Saal and Bensmaia, 2014) and different
sub-modality compositions of sensory afferents (mix of slowly
adapting (SA), rapidly adapting (RA), and pacinian corpuscles
(PC) afferents). Thus, by evoking sensations through different
electrodes at different locations in a phantom or residual limb,
the field of projections of active fibers, which is the area
where sensation is felt, can be determined. These sensations
can also be determined through a sub-modality composition
of activated fibers. For instance, if afferent populations of SA,
RA, or PC fibers are stimulated, the evoked sensation will be
of pressure, flutter, or vibration, respectively (Torebjork et al.,
1987).
Moreover, the pulse shape is of extreme importance. The
electrical pulse is transmitted to the nerve fiber using implanted
electrodes. The field of pulse magnitude decays as the distance
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Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces
from the implanted electrode increases. Rattay and Aberham
(1993) concluded that nerve activation is proportional to the
rate of change of the voltage along the axons. Therefore, large,
myelinated and closer axons can be activated by square pulses
before small, unmyelinated and distant axons. A ramp pulse can
be used to activate distal axons before closer ones (Grill and
Mortimer, 1997; Grill et al., 2009), while a quasi-trapezoidal pulse
have been employed for recruitment of smaller axons before
larger ones (Fang and Mortimer, 1991). In implanted devices,
exponential-shaped waveforms, utilizing minimal energy, can be
used to minimize the power requirement for activation of larger
and closer axons (Wongsarnpigoon and Grill, 2010; Krouchev
et al., 2014).
As explained above, providing stimulation using different
electrodes can elicit sensation of several different qualities. In
addition to the closeness of intra-neural interfaces physically,
another powerful tool is the design and manipulation of
the spatiotemporal electro-magnetic field using extra-neural
interfaces. These interfaces, with the help of multiple electrodes
(Schiefer et al., 2008), generate an electrical field, the variation of
which can change the activation areas of sensory percepts on an
artificial hand/arm (using controlled inputs). The standard nerve
stimulation paradigm is a train of identical, charge-balanced,
square electrical pulses characterized by pulse amplitude, pulse
width, and pulse repetition period (or inter-pulse interval).
Traditionally, these three parameters are time-invariant and fixed
in value. The intensity of a pulse can be manipulated in three
different ways by varying pulse width (Tan et al., 2014), pulse
frequency (Dhillon et al., 2005), and pulse amplitude (Raspopovic
et al., 2014). It ranges from the lowest charge that can evoke
a sensation all the way up to the value that elicits unnatural
sensation (or paresthesia). By alteration of these stimulation
parameters, sensations of varying quality can be evoked. The
details of stimulation parameters used in different human studies
for elicitation of sensory feedback are tabulated in Table 1.
In somatosensory applications, intensity is proportional to the
variation of the stimulation frequency, and the resulting sensory
perceptions can be changed with bursting pulse trains (Tan
et al., 2015). In the patterned frequency paradigm, synchronous
activity in a population of axons can be generated by maintaining
the strength of the stimulation pulse constant. However, this
cannot help in distinguishing complex sensory information.
For generation of a patterned fiber activity, several modalities
such as patterned field distribution of stimulation and patterned
stimulation intensity have been employed. In each paradigm,
non-synchronous activity in a population of axons is generated
by creating a shift in the field between stimulation pulses.
This information, if controlled properly, can be utilized for
the restoration of several different somatosensory percepts. The
nerve stimulation has been shown to induce different types of
sensations; for instance, proprioceptive and touch sensations.
Complex qualities of touch such as vibration on the skin, tingling,
tapping, stinging, brushing, and itch have been observed during
experimentation (Tan et al., 2014). Stimulation can infrequently
evoke proprioceptive sensations, for example, sensations of
movement of a finger or joint or a specific hand configuration.
Systematic study of percept qualities and gradedness of these
sensations have not been completed yet. Data on contact forces
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between the perceptually available object and the skin are
required for grasping and manipulating an object (Johansson
and Flanagan, 2009), because too much force is likely to damage
the prosthesis or the object, while too little force can cause
slippage (Witney et al., 2004). In previous studies, contact force
has been manipulated through the intensity of stimulation; hence
the greater intensity and the greater contact force.
RESTORATION OF SOMATOSENSORY
FEEDBACK THROUGH CORTICAL
STIMULATION
Although this review has focused more on peripheral-nerve
recording, stimulation, and the methods adopted for encoding,
we will briefly address the stimulation issue in the cortical
region for restoration of somatosensory feedback. The field
of neuroprosthetics has reached an age of maturity. When
similar questions need to be addressed, different strategies are
pursued (Courtine and Bloch, 2015). Elicitation of percept by
electrical stimulation was first demonstrated by Wilder Penfield
in 1937. They showed that somatosensory cortex (S1) neurons are
organized into separate columns representing different regions of
the body (Penfield and Boldrey, 1937). They later induced tactile
sensation by applying electrical stimulation to the somatosensory
area of a neurosurgical patient (Rasmussen and Penfield, 1947).
The locations of percept areas on a body are systematically the
same as those required on the cortical surface of the brain for
stimulation (Rasmussen et al., 2009). The neurons that can react
to similar types of stimuli have their functional columns in area
S1. Studies also have found evidence for S1 regions that encode
sensory information for individual digits of a hand along with
different types of receptors within those digits (Merzenich et al.,
1983; Sur et al., 1984).
Intra-cortical microstimulation (ICMS) has been applied to
primates having the pulse frequency corresponding to the evoked
perception of cutaneous flutter (Romo et al., 1998), which depicts
the temporal configuration of pulse-train shape for elicitation
of different sensations in a systematic mode. Recently, several
studies have demonstrated the characteristics of ICMS by varying
the parameters of stimulation for sensory feedback and recording
of voluntary movements in primates (Fitzsimmons et al., 2007;
O’Doherty et al., 2011; Zaaimi et al., 2013; Higo et al., 2016) and
rodents (Fridman et al., 2010). Another important breakthrough
is the first bidirectional brain-machine interface in which a signal
from the motor cortex of a non-human primate was able to
control the cursor, while stimulation is applied simultaneously to
the brain area S1 to give sensory feedback on the movements,
though the primate had to go through a learning process for
mapping the afferent interface (Rajan et al., 2015; Tabot et al.,
2015). These research advances have highlighted several methods
of evoking broader ranges of near-to-natural sensations of touch
and proprioception (Blank et al., 2010), with the hope that they
will prove applicable to individuals who have lost limbs and/or
senses. Somatosensation including proprioception is an essential
element of natural motor abilities. Without having the sense of
proprioception, it is difficult to plan a dynamic limb movement
(Sainburg et al., 1995); indeed, an experiment on a monkey with
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Intra-neural Recording and
LIFEs
stimulation
Dhillon and
Horch, 2005
11
Intra-neural Stimulation
TIMEs
Median and
ulnar nerves
Median and
ulnar nerves
Horch et al.,
2011
Raspopovic
et al., 2014
Median ulnar
nerves
Rossini et al., Intra-neural Recording and
2010
tf-LIFE4
stimulation
Intra-neural Stimulation
LIFE
8
2 each
4 each
2 each
4 each
1
2
1
6
8
4-weeks
9-days
4-weeks
14-days
2-weeks
2-days
Selectivity
and longevity
Train
duration
Touch
sensation
Touch, finger
position and
force
Touch
Grip force,
touch and joint
position
Touch,
pressure and
joint position
Nil
290-µs
0.3-s
Pulse
width
Current
intensity
300-µs Nil
10–250 Hz
(Position)
10–500 Hz
(Pressure)
Nil
Nil
Frequency
75-µs
Rectangular Nil
cathodal
Biphasic
160–
240 mA
Upto 50 Hz
Up-to 200 30–200 Hz
µA
(proprioception)
20–170 Hz
(touch)
Rectangular 10–300 10–100 µA 10–500 Hz
cathodal
µs
Charge
balanced
300-µs 1–200 µA
Monophasic, 250-µs Up-to
capacitively
200-µA
coupled,
or biphasic,
chargebalanced,
rectangular
Pulse train
Stimulation parameters
500-ms (5s Charge
between
balanced
successive
trains)
Touch and
Selectivity only 500-ms
proprioception
No. of Period of Sensory
subjects study
feedback
modality
2 each
No. of
interfaces
Median nerve 4–8 each
Severed
median and
ulnar nerves
Intra-neural Recording and
LIFEs
stimulation
Placement
Dhillon et al.,
2005
Mode
Intra-neural Acute recording Severed
LIFEs
and stimulating median and
ulnar nerves
Type
Interface configuration
Dhillon et al.,
2004
Study
TABLE 1 | Summary of human neural implant studies and modalities for restoration of sensory feedback.
(Continued)
Bidirectional,
near-to-natural control
of a hand with stable
tactile feedback
Successful
discrimination of
objects using artificial
tactile and
proprioceptive
feedback without visual
or auditory cues
Real-time control of
three motor
movements and
discrete tactile
sensations achieved for
first 10 days
Direct neural feedback
and control of an
artificial arm having
ability to judge finger
force, static elbow, and
finger position without
visual feedback
Control of prosthetic
arm has been improved
with experience and
training of several days.
Showed stable
sensation
Recording of volitional
motor commands
along with the
elicitation of discrete,
unitary and graded
sensations of touch,
joint movement and
position
Findings
Ghafoor et al.
Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces
October 2017 | Volume 11 | Article 59
Type
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12
Intra-neural Chronic
USEA
recording and
stimulation
Intra-neural Acute
TIME
stimulation
Oddo et al.,
2016
Extra-neural Chronic
stimulation
FINE and
Cuff
Chronic
recording and
stimulation
Mode
Median nerve 1
Median and
ulnar nerves
1 each
1
2
4-weeks
4-weeks
Touch
Up-to
86-unique
sensory
percepts
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