(2020) 19:81
Milosevic et al. BioMed Eng OnLine
https://doi.org/10.1186/s12938-020-00824-w

REVIEW

BioMedical Engineering
OnLineOpen Access

Why brain‑controlled neuroprosthetics
matter: mechanisms underlying electricalstimulation of muscles and nerves
in rehabilitation
Matija Milosevic1* , Cesar Marquez‑Chin2,3,4, Kei Masani2,3,4, Masayuki Hirata5, Taishin Nomura1,
Milos R. Popovic2,3,4 and Kimitaka Nakazawa6*Correspondence:
matija@bpe.es.osaka‑u.ac.jp
1
Graduate Schoolof Engineering Science,
Department of Mechanical
Science and Bioengineering,Osaka University, 1‑3Machikaneyama‑cho,Toyonaka, Osaka 560‑8531,
Japan
Full list of author information
is available at the end of thearticleAbstract
Delivering short trains of electric pulses to the muscles and nerves can elicit action
potentials resulting in muscle contractions. When the stimulations are sequenced
to generate functional movements, such as grasping or walking, the application isreferred to as functional electrical stimulation (FES). Implications of the motor andsensory recruitment of muscles using FES go beyond simple contraction of muscles.
Evidence suggests that FES can induce short- and long-term neurophysiologicalchanges in the central nervous system by varying the stimulation parameters anddelivery methods. By taking advantage of this, FES has been used to restore voluntary
movement in individuals with neurological injuries with a technique called FES therapy(FEST). However, long-lasting cortical re-organization (neuroplasticity) depends on theability to synchronize the descending (voluntary) commands and the successful execu‑
tion of the intended task using a FES. Brain-computer interface (BCI) technologies offer
a way to synchronize cortical commands and movements generated by FES, which canbe advantageous for inducing neuroplasticity. Therefore, the aim of this review paper
is to discuss the neurophysiological mechanisms of electrical stimulation of muscles
and nerves and how BCI-controlled FES can be used in rehabilitation to improve motor
function.
Keywords: Brain-computer interface (BCI), Functional electrical stimulation (FES), FES
therapy (FEST), Hebbian plasticity, Neuroplasticity, Rehabilitation

Background
Electrical stimulation can be used to deliver short electric impulses to individual orgroups of muscles to cause action potentials under the stimulating electrodes, consequently producing muscle twitches and limb movements. Clinical applications of electrical stimulation first appeared in the 20th century (e.g., [68, 75, 84], to name a few). Withsignificant developments of stimulation technology and electronic circuits, safe applications of electrical stimulation of muscles can now be used to generate controlled limb
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Milosevic et al. BioMed Eng OnLine(2020) 19:81

movements in individuals who have sustained central nervous system (CNS) injuries
such as spinal cord injury (SCI) or stroke [116].
Individuals with CNS injuries are unable to generate and/or transmit voluntary
motor commands to their muscles, resulting in reduced ability to control their limbs.
This paralysis can affect their ability to produce functional movements such as reaching and grasping. Neuromuscular electrical stimulation (NMES) is a technique that canbe used to activate muscles artificially and produce individual joint movements whenvoluntary control is affected due to CNS injury. When electrical stimulation is patterned
and temporally sequenced to generate coordinated limb movements, this is referred to
as functional electrical stimulation (FES). Overall, FES systems fall into the category of
motor neuroprostheses [119], which are devices that use electrical stimulation to activate paralyzed muscles in a functional manner to generate limb movements [118]. While
motor neuroprosthesis could include spinal cord stimulation and deep brain stimulation
systems, this review will primarily focus on FES devices for stimulation of muscles andnerves. Clinical use of FES neuroprostheses includes, but is not limited to, restorationof upper and lower extremity functions, bladder and bowel functions, and respiratory
function [108, 119]. Typically, FES neuroprostheses were designed to be worn as permanent assistive devices, which an individual can use to perform otherwise impaired functional movements. Such application is refereed to as prosthetic use. However, in recent
years, evidence has demonstrated that application of FES over a period of time could
help individuals with neurological impairments regain some of the voluntary function.
By taking advantage of this therapeutic effect, FES has been used to restore voluntary
upper-limb movements in individuals with neurological injuries using FES therapy(FEST) [59, 116, 144]. It should be noted that subjects are asked to attempt each movement during FEST, while FES is applied by the therapist to assist movement completion.
Such associative interventions, that combine cortical activations and peripheral stimulation, likely involve Hebbian learning principles [52] to induce experience-dependantcortical re-organization (neuroplasticity) within the CNS.
Recent developments of non-invasive brain recording and processing [95] have
impacted the expansion of brain-computer interface (BCI) technologies [152]. Whileinvasive BCI-FES applications can facilitate restoration of movements [2, 22], noninvasive applications can be used for improving motor function through rehabilitation.
Indeed, applications of BCI for improving motor function through rehabilitation are fast
emerging [20, 30, 72]. Specifically, BCI systems translate brain signals into novel outputs,
which can also be used to effectively synchronize cortical commands and movements
generated by FES. Synchronized activations of cortical and peripheral networks may
also facilitate associative Hebbian learning. Indeed, recent applications in rehabilitation of CNS injuries are starting to show convincing evidence of cortical neuroplasticity
and improved motor function after use of BCI-controlled FES [17, 34, 58, 66, 74, 80, 93,107]. However, despite evidence supporting recovery of voluntary function after FEST
and BCI-controlled FES, little is known about the changes that occur in the CNS during and after electrical stimulation of muscles and nerves and why synchronization ofcortical and muscle activations through BCI may be relevant in rehabilitation. Therefore,
the objectives of this review paper are to: (A) introduce the underlying basis for generating muscle contractions using FES (Sect. “Electrical stimulation of muscles and nerves”);

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Milosevic et al. BioMed Eng OnLine(2020) 19:81(B) summarize the underlying therapeutic and neurophysiological effects resulting fromtherapeutic application of FES (Sect. “Effects underlying electrical stimulation of muscles and nerves”); and (C) discuss the mechanisms of associative stimulation of muscles
and nerves through application of BCI-controlled FES in rehabilitation (Sect. “Braincontrolled electrical stimulation of muscles and nerves in rehabilitation”). Specifically,
the focus of this review will be to provide the underlying mechanisms and implications
for the development of rehabilitation technologies using BCI-controlled FES to improve
upper-limb voluntary motor function.

Electrical stimulation of muscles and nerves
Delivery of electrical stimulation

Electrical stimulation can be delivered in multiple ways, including transcutaneous andsubcutaneous systems. Subcutaneous systems are typically used for applications such as
bladder voiding and hand function. Such systems are be able to target the muscles more
precisely and generally should require lower stimulation intensities, while invasive procedures could be more prone to infections [108].
Transcutaneous systems, which will be the focus of this article, are most frequently
used for NMES, FES, and FEST to activate the motor system [119]. Electrical stimulators create a potential difference between two electrodes, a positive anode and a negative
cathode, using surface (transcutaneous) stimulation electrodes [118]. The latest generation of FES systems are usually current regulated (compared to voltage regulated systems) as they can ensure that a fixed amount of charge is delivered to excitable tissue
regardless of the impedance of the electrode-tissue interface. As illustrated in Fig. 1a,electrodes can be placed on: (i) the skin surface over the nerve trunk, which is referred
to as peripheral nerve stimulation (PNS); or (ii) the belly of the targeted muscle, whichreferred to as motor point stimulation (MPS). During both PNS and MPS, stimulation of the peripheral nerves and muscle belly activate the mixed nerves (nerves that
contain both motor and sensory fibers). Specifically, stimulation over the muscle belly
activates nerves that contain both motor fibers and muscle spindle afferents. Moreover,nerve trunk stimulation targets the peripheral mixed nerves that contain and simultaneously activate both sensory (afferent) and motor (efferent) nerves. While H-reflex and
M-wave recruitment patterns may differ between MPS and PNS [11, 100], in practice,during stimulation to produce functional movements, simultaneous efferent and afferent
recruitment is expected. Moreover, muscles with the nerve trunks accessible for transcutaneous electrical stimulation are limited, e.g., trunk muscles cannot be activated via
the nerve trunk stimulation [86]. In this case, muscle belly stimulation over the motor
point is needed, which typically requires considerably higher stimulation amplitudes [8].
To generate muscle contraction, the impedance under the electrodes, as well as thelocation, size and orientation of the electrodes are important for optimizing the current [37]. Having a smaller cathode electrode and placing it close to the target nervewith the larger anode placed a distance away from the cathode can be used to generate more specific/accurate stimulation localization under the cathode while allowing a larger area of the skin under the anode to be used to close the electrical circuitand minimize discomfort under the cathode. Empirically, it is well-known that there
are locations, where muscles are most sensitive to electrical stimulation, i.e., motor

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Fig. 1 a Neurophysiological mechanisms of electrical stimulation of muscles and nerves—Illustration of theperipheral pathway generated via the efferent (motor) volley, and afferent (sensory) pathways, generated
via the sensory volley during functional electrical stimulation (FES) of muscles and nerves. The antidromic
activation along the motor axons and the sensory feedback traverses the spinal cord and activates thesensorimotor cortical networks to synapse with the cortical (descending) signals from the brain when abrain-computer interface (BCI) is used to trigger electrical stimulation. The figure shows electrode placementon the nerve trunk—peripheral nerve stimulation (PNS; black anode) and on the muscle belly—motor point
stimulation (MPS; gray anode). b BCI activation of electrical stimulation—Illustration shows the componentsof the BCI system that can be used to trigger electrical stimulation of muscles and nerves via FES using
non-invasive brain oscillatory recordings through electroencephalography (EEG). The main components
include: (1) calibration of the state decoder (offline); and (2) control of FES system in real-time (online). During
online control of FES, participants should perform functional tasks

points (see [14] for upper limbs; [10] for upper body; and [21] for lower limbs). Larger
muscles are known to have several motor points (e.g., seven quadriceps motor points
were identified [21]), while smaller hand muscles typically have one optimal motor
point [14]. Placement of electrodes on the motor point also plays an important role ingenerating strong muscle contractions [45].

Physiological basis for generating muscle contractions using electrical stimulation

When a potential difference between the surface electrodes is created, the anode (“positive” terminal) attracts the negative and repels the positive ions towards the cathode(“negative” terminal), which attracts the positive and repels the negative ions. In effect,a current flow is created from the anode towards the cathode, which delivers an electriccharge to the nerve trunk. At rest, the membrane potential of nerve cells and muscle fibers is approximately –70 to –90 mV with respect to the extracellular fluid. Applying electrical stimulation to the nerve trunk or muscle fibers can induce an electrical charge in
the immediate vicinity of the outer membrane of the cell and change the rest membranepotential, which can in turn artificially elicit action potentials that can produce muscle
contractions [8, 116]. While voluntary contraction induces action potential firing ratearound 4–12 Hz [81], a higher stimulation frequency, around 20–50 Hz, is required toinduce tetanic muscle contractions (see Sect. “Pulse frequency”).

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When the nerve trunk is stimulated using electrical stimulation, both of the motor
nerves (efferent nerves descending from the CNS to the muscles) and the sensory nerves(afferent nerves ascending from the sensory system to the CNS) are activated, as illustrated in Fig. 1a. Motor nerve activation generates direct contractions of the innervatedmuscles, while the sensory nerve activation can indirectly generate muscle contractions
by eliciting the spinal reflex. Moreover, sensory stimulation, involving the reflex pathways, is thought to be beneficial for inducing neuroplasticity in the CNS [13, 41]. Whenelectrical stimulation is applied over the muscle belly, the recruitment order is random,
since activations depend on the distance between the electrodes and the nerve end terminals as well as the thickness of the nerve fibres [13]. Stimulation on the muscle belly
activates localized muscle fibres around the electrodes, while nerve trunk stimulation
activates muscle fibres evenly in the entire muscle belly [13]. Moreover, since muscle
belly stimulation activates localized muscle fibres around the electrodes, repeated stimulation can also induce muscle fatigue [12, 81].
During electrical stimulation of muscles and nerves, electrical impulses propagate
orthodromically along the motor axon towards the muscle to generate muscle contractions (motor volley in Fig. 1a). However, impulses can also propagate antidromicallyalong the motor axon, away from the muscles towards the CNS (antidromic activationin Fig. 1a). This bidirectional propagation is unique to electrical stimulation and does
not occur during voluntary activation of muscles. Antidromic activation along the motor
nerves is generally considered to be a side effect of electrical stimulation. However, it has
also been hypothesized that such antidromic propagation may play a role in neuroplasticity during electrical stimulation [126].
Stimulation parameters

Different waveforms can be used to generate electrical impulses to stimulate the muscles. Galvanic (direct) current is not appropriate for generating neuromuscular contractions because it only produces an action potential at the moment it is turned on andoff. More common are alternating currents waveforms, which deliver short electric
impulses. Monophasic waveforms are disadvantageous as they could cause accumulation of electric charge in the tissue during prolonged electrical stimulation [8, 81]. Balanced biphasic impulses ensure that all residual charge left in the tissues is removed [8,81]. However, such pulses generate contraction under both the anode and the cathode.
Currently, sophisticated stimulation systems use asymmetric balanced biphasic impulses
to ensure that the muscle contractions occur only under the cathode [81]. The magnitude of muscle contractions can be varied by changing the stimulating pulse amplitude(A), pulse width (PW) or the pulse frequency (f ). Varying these parameters has beenshown to have different neurophysiological effects during recruitment of motor and sensory pathways.
Pulse frequency

The action potentials in the CNS are frequency modulated, meaning that the intensityof the transmitted signal is proportional to the number of action potentials that occurs
per unit time. Typical frequency of the nerve firing is around 4–12 Hz and the firingof the nerve fibres is asynchronous [81]. Depending on the application, a variety ofPage 5 of 30

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frequencies can be used to generate contractions with FES. The most typical frequencies
used in clinical applications range between 20 and 50 Hz [8]. These higher frequencies
are needed, because electrical stimulation activates muscle fibres synchronously and assuch requires higher firing rates to generate tetanic contractions [8]. Moreover, lower
frequency stimulations (< 16 Hz) produce unfused contractions. They could also induce
low-frequency fatigue, and they may not always be sufficient to elicit strong contractions [37]. On the other hand, high frequency stimulation (50-80 Hz) can induce rapid
onset of muscle fatigue, which is a significant limitation of electrical stimulation systems[37]. However, higher frequencies of stimulation were reported to be more comfortable,
because the response is smoothed [8]. Mang et al. [76] showed that high frequenciesof peripheral stimulation at 100 Hz had larger central contributions, suggestive of having short-term neuromodulatory effects, compared to lower frequencies, which had noeffect. Therefore, careful selection of stimulation frequencies can have a critical impact
for inducing neurophysiological changes in the CNS during electrical stimulation. In our
experiments, we typically used stimulation frequencies between 20 and 40 Hz for activation of upper-limb [58, 59, 62, 80, 88, 144], lower-limb [87, 147], as well as trunk muscles[86].
Pulse amplitude

The pulse amplitude, or the intensity, by which the stimulation is delivered is related tothe depolarizing effect, with higher amplitudes inducing a stronger depolarizing effect.
Typical FES pulse amplitudes rarely exceed 100 mA, while the exact levels depend on
muscle properties, including the size of the muscle as well as the size of the stimulating
electrodes and the pulse width of the simulating waveform. Smaller upper-limb muscles
typically require smaller electrodes and lower pulse amplitudes to be contracted (e.g.,
10–20 mA in [88]), while larger lower-limb and trunk muscles typically required larger
amplitudes (e.g., 20–35 mA for contracting the soleus muscle in [87] and 20–25 mA forcontracting the erector spinae muscle in [86]). Increasing the stimulation amplitude
results in additional recruitment of smaller fibers near the electrode and larger fibers farther from the electrode [85]. With increasing amplitude, a threshold is reached beyond
which no further fibers can be recruited, and no additional torque generated by the muscles. Moreover, very high intensities could lead to rapid muscle fatigue and discomfort
during FES [8]. On the other hand, it was suggested that lower intensity stimulation(sensory stimulation) is more effective in inducing central changes in the CNS comparedto higher intensities [13]. However, higher amplitudes of stimulation could be related tothe increase in strength after FES training [37, 131].
Pulse width

Pulse width, or pulse duration, is the time span of a stimulating pulse. To achieve adequate depolarization of the nerve cells and cause muscles to contract, sufficient pulse
width is required. Typical FES pulse width in clinical applications is between 200 and
500 μs. Short pulse durations (10–50 μs) have been shown to be selective in activationof muscle nerves, which can generate larger torque with a small number of muscle fibers [48]. However, very short pulse durations require larger pulse amplitudes to achieveadequate depolarization to contract the muscles. Larger pulse width was shown toPage 6 of 30

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produce stronger contractions [71], in addition to being able to penetrate deeper into
subcutaneous tissue [23]. Longer pulse duration stimulation was found to be more effective for promoting central activation [5], likely due to activation of sensory axons [32,33]. Preferential activation of motor axons using shorter pulse duration stimulation [48]and sensory axons using longer pulse duration [32, 33, 65] is probably related to thestrength-duration constant of the sensory and motor axons [89, 146].

Effects underlying electrical stimulation of muscles and nerves
Growing evidence suggests that FES can cause short- and long-term neurophysiological changes in the spinal and cortical neural circuits [26, 41]. Initially, FES was mostly
employed as a permanent neuroprosthesis to regain function of paralyzed muscle (prosthetic use). It wasn’t until later that scientists seriously started to investigate the neurophysiological changes and gather evidence to show spinal and cortical re-organization
after electrical stimulation. This evidence suggests that increased excitability can last
anywhere from 5 min to several hours after a single session of electrical simulation [13],and for as long as several days after multiple sessions [83]. Moreover, recent results fromFEST suggest that long-term clinical benefits could be obtained after use of electricalstimulation [59, 115, 116, 144]. Evidence also points out that therapeutic effectiveness isaccompanied by long-lasting re-organization in the CNS [26, 88, 127, 132].
Therapeutic effects

FES has been utilized as a prosthetic to restore various motor functions, such as standing (e.g., [1, 147]), sitting (e.g., [86, 145]), reaching and grasping (e.g., [2, 22, 96]), andmore. In addition, it was also demonstrated that application of FES can have carry-overeffects even after the stimulation was turned off. This phenomenon was first noticed by
Merletti et al. [84]. Specifically, Merletti and colleagues observed that 2 months of stimulation of hand muscles using FES could improve the voluntary functional performancein some patients. Based on those findings, researchers started using FES as a therapeutic
tool. Such interventions have been referred to as FEST [81, 114, 116]. Clinical improvements in reaching and grasping function after FEST was demonstrated in individuals
with stroke and incomplete SCI [59, 79, 144]. However, despite clinical success of FEST,the exact mechanisms of the observed carryover effects are still not fully understood.
It is believed that the clinical changes after FEST could partially be due to the muscle training and strengthening as well as the improved flexibility and range of motionof the affected limbs [116]. In addition to the peripheral effects, it is thought that the
spasticity reduction after FEST is one of the main reasons for clinical improvements inmotor function [47, 62, 87]. However, compelling evidence of cortical brain re-organization after FEST has also been demonstrated recently [88, 132, 127, ]. It is hypothesizedthat the main mechanism behind the neuroplasticity induced by FEST, is the involvement of the voluntary intent during training [116]. During FEST, subjects are asked toattempt the movement, and only after their attempt, FES is applied on the muscles toassist movement completion. This idea was proposed by Popovic and colleagues [117].
Similarly, Rushton [126] suggested that the coincidence of the ascending and descending signals plays a significant role for enhancement of the synaptic connections. Specifically, during FES activation of muscles, antidromic impulses are also sent in the opposite

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direction along the motor axons towards the spine and the brain at the same time as thesensory volley is generated along the sensory axons (Fig. 1a). When the subject attempts
the movement voluntarily, the descending motor commands are sent from the brain to
the spinal motor neurons. It is thought that repetitive coincidence of these ascending(antidromic activation of motor axons and the sensory feedback) and descending signals
over the course of multiple trials is responsible for the FEST-induced neuroplasticity.
Next, we will discuss the neurophysiological effects of electrical stimulation of muscles
and nerves, before returning to mechanism of associative neuroplasticity.
Neurophysiological effects
Spinal reflex circuits

Evidence from studies examining the spinal reflex excitability (e.g., H-reflex), suggestthat both short-term and long-term changes can be induced in the spinal circuits after
application of electrical stimulation [13]. FES applied at intensities above the motor
threshold generates tetanic muscle contractions via the efferent pathway, which may also
antidromically activate the Renshaw cells [126] to inhibit spinal reflex excitability after
the stimulation [53, 62, 87]. For instance, electrical stimulation was shown to reduce
stretch reflex excitability in individuals with neurological deficits, which is beneficial for
reduction of spasticity [47]. Specifically, as little as 60 s of FES applied over the soleus
muscle, at intensities that evoked muscle contractions, can inhibit spinal reflex excitability in both the stimulated and non-stimulated muscles as well as in homologous andnon-homologous contralateral muscles for at least 15 min after the intervention, while
voluntary contraction-induced effects did not outlast the stimulation period and sensory-level stimulation did not affect spinal reflex excitability [87].
Prolonged application of electrical stimulation could also lead to long-term increase inreciprocal inhibition [105, 109], which may be beneficial for rehabilitation of individuals
with CNS injuries. Moreover, in the upper-limb muscles, Kawashima et al. [62] showeda reduction in upper-limb spasticity indicated by the inhibition of H-reflex excitability
after intensive upper-limb FEST intervention, which was accompanied by improvements
in upper-limb motor outcomes. Unilateral electrical stimulation-induced contractions ofupper-limb muscles can also affect spinal reflex excitability of homologous muscle pairs
in the contralateral arm [53], as well as the lower-limbs bilaterally if the stimulation is
combined with voluntary muscle contractions [60]. Therefore, electrical impulses that
activate the mixed nerve bundle recruit not only the efferent motor axons to generatemuscles contractions, but also the afferent sensory nerves via muscle stretch-induced
feedback (muscle spindles) [13] or via antidromic propagation along the motor axons[126], which can affect both short-term and long-term spinal reflex excitability after the
stimulating period.
Corticospinal tract

Electrical stimulation was also shown to affect the corticospinal excitability, which canbe elicited using motor evoked potentials (MEPs) through single-pulse transcranial
magnetic stimulation (TMS) applied over the primary motor cortex. A large body ofliterature has investigated how different modes of delivery and parameters of electricalstimulation can affect changes in corticospinal excitability (for a review, see [13, 26]).

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Increased MEP responses were observed following median nerve stimulation appliedat the wrist when the stimulation was above, but not when it was just below, themotor threshold intensity [128]. Similarly, mesh glove stimulation, which targets
widespread engagement of afferent fibers, was shown to increase corticospinal excitability for up to 1 h when it was applied at the sensory threshold intensity, but not at
lower intensities [46]. Extent of corticospinal excitability modulation may also dependon the stimulation frequency. Using sensory level of stimulation, Mang et al. [76]showed that 100 Hz stimulation (high frequency) could increase corticospinal excitability, but they also reported that 200, 50, and 10 Hz stimulating frequencies were
ineffective. When applied at motor threshold intensities, larger corticospinal modulation effects were obtained at higher frequencies, including 100 Hz and 20–50 Hz,
compared to the 10 Hz stimulating frequency [56]. Generally, stimulation that can
induce voluntary-like activations seem to be necessary to cause reliable changes
in the CNS [31]. However, even at such intensities, no modulation of corticospinal
excitability was observed immediately after each 45–60 min FEST session, while
the cortical silent period was affected, implying short-term effects on cortical and/or subcortical inhibition [88] and involvement of sensorimotor integration [153]. It
was recently shown that duration of FES delivery can also alter corticospinal modulation, with 20–40 min of stimulation facilitating MEP responses, while 60 min having
no effects on corticospinal excitability [3], possibly due to effects of fatigue. Overall,
the abovementioned studies point out that motor-level stimulation that can induce
voluntary-like contractions, and stimulation delivered at higher frequencies that produce fused muscle contractions (not too high as to induce rapid muscle fatigue) areneeded to evoke reliable excitability with the CNS.
Repetitive application of lower-limb electrical stimulation over multiple sessions wasshown to increase corticospinal excitability after the stimulating period in the lowerlimbs [143]. Moreover, increased corticospinal connections were shown following prolonged use of a drop-foot stimulator, which was accompanied with improvements inwalking speed in people after stroke and multiple sclerosis [38]. In the upper-limbs,
two hours of electrical stimulation was also successfully used to increase corticospinal excitability after the intervention, while aftereffects were relatively short-lived [122,124]. However, longer-lasting changes in corticospinal excitability can be induced after
approximately 40 h of FEST, with carryover effects outlasting the intervention in individuals with stroke [132, 127] and traumatic brain injury [88]. Overall, reinforcementof connections between the brain and the upper- and lower-limb muscles is generally
associated with improvements in functional outcomes [13, 26, 134]. However, use of FES
could also improve clinical outcomes, without any measurable changes in corticospinal excitability [9]. Overall, stimulation parameters have varied considerably between
studies, which could significantly alter the neurophysiological effects [31, 42]. Similarly, effects of stimulation may be different between the upper- and lower-limb muscles[13], which could be due to their unique functional roles and different neural pathways
involved in controlling these distinct segments. Nonetheless, careful selection of parameters is imperative for delivering effective neurophysiological changes both in the shortterm during and after the stimulating period as well as to induce long-lasting sustainedchanges.

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Cortical networks

Although electrical stimulation could affect excitability of both the spinal and the cortical networks, it is believed that short- and long-term excitability of the cortical sensorimotor networks can be engaged during and after electrical stimulation of muscles andnerves [26, 29, 41, 64, 122]. Specifically, 2 hours of electrical nerve stimulation applied at
the wrist was shown to produce larger areas over which MEP responses can be evoked[124]. After traumatic brain injury, 3 months of FEST was required to elicit bigger MEP
representations in the motor cortex, while cortical changes may also sustain after theintervention [88]. Motor maps obtained using TMS-evoked MEP responses can reliably
extract somatotopic information from the primary motor cortex [148], providing evidence for cortical-level re-organization after application of FES [50].
This is further supported by recent evidence from neuroimaging studies showing thatthe state of motor cortical networks after application of FES is altered through multistage hierarchical processing which engages various parts of the motor system [6]. The
somatosensory cortices, including both the primary (S1) and secondary (S2) somatosensory areas, are first activated during electrical stimulation of muscles and nerves [19, 26,
67, 103]. At intensities above the motor threshold, electrical stimulation activates cutaneous afferents as well as muscle spindles [35] to engage the S1 area [26, 151]. Activations in S2 appeared at lower intensities compared to S1 [7], while S1 activation was
proportional to the intensity of stimulation [69]. Afferent recruitment via electrical stimulation also seems to have intensity-dependant modulation not only in the somatosensory cortex, but also in the primary motor cortical (M1) area, with larger activations at
higher stimulating intensities compared to sensory-level stimulation [133]. As expected,
cortical activation levels were larger during voluntary muscle contractions, compared to
FES-induced movements in the M1, S1, and the supplementary motor area (SMA) [57].
However, S2 area activations were larger during FES-induced contractions, which may
suggest possible direct activations [57].
Functional levels of stimulation, which can generate voluntary-like wrist flexion andextension, resulted in simultaneous cortical activations in the contralateral M1, S1 andpremotor (PM) areas, bilateral S2 and SMA, as well as ipsilateral cerebellum activations[18]. Similarly, long-term FEST delivery induced widespread cortical re-organization
characterized by increased contralateral cortical activations, as well as a similar trend
in ipsilateral hemisphere activations, compared to pre-intervention assessments [88]. In
stroke patients, 3 months of FEST resulted in either widespread activations distributed
bilaterally in the somatosensory areas or more focused unilateral somatosensory activation after the intervention [127]. Similarly, FEST improved motor function in chronic
stroke patients, which was accompanied by shifting in the somatosensory area activations from ipsilateral to contralateral hemisphere after the intervention [132]. Alteredcortical activations in stroke patients after using FES were also shown in the lower-limbs
with drop-foot stimulation, suggesting that SMA and angular gyrus regions play animportant role in mediating carryover effects [43]. Moreover, short-term lower-limb FES
application elicited significant activations of the sensorimotor networks (i.e., cerebellum and thalamus), with different neural activations achieved by adjusting the stimulation parameters [149]. Therefore, it seems that somatosensory cortex activations can berelayed to the motor cortical areas via cortico-cortical and/or cerebello-thalamo-cortical

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connections during electrical stimulation of muscles and nerves [26]. These results,
therefore, suggest that peripheral electrical stimulation-induced activation of muscles
can engage cortical sensorimotor networks in the widespread brain areas both during
and after stimulation delivery.

Brain‑controlled electrical stimulation of muscles and nerves in rehabilitation
Now that we understand that electrical stimulation of muscles and nerves can engage
the central nervous system, a question remains: how such changes can be utilized tomaximize neuroplasticity? The answer may very well be related to associative stimulation and adjuvant techniques for electrical stimulation of muscles and nerves, specifically through brain-computer interface (BCI). During FEST delivery, participants are
asked to actively attempt each movement and contraction before the therapist triggers
the sequence of appropriate muscle activations using FES to assist task performance.
Such task-specific and repeated training delivered with assistance of a therapist emphasizes the importance of associative interventions that combine activations at the corticallevel and peripheral stimulation [88, 117] to induce experience-dependant cortical neuroplasticity [104]. This form of associative stimulation likely involves Hebbian plasticity[52], where a presynaptic input onto a postsynaptic neuron is strengthened as a consequence of simultaneous activation at the pre- and postsynaptic terminals [26]. Associative stimulation techniques that combine cortical and peripheral activations may include
experimentally induced non-invasive brain stimulation, voluntary muscle contractions,motor imagery, and BCI control of electrical stimulation of muscles and nerves. Below,
we will provide a brief overview of the neurophysiological mechanisms of these different forms of associative stimulation, while the main focus will remain on BCI-controlled
FES.
Associative stimulation of muscles and nerves

Adjuvant associative techniques that combine central activation at the level of the cerebral cortex and muscle contractions via electrical stimulation can be used to promote
CNS neuroplasticity. Non-invasive brain stimulation can experimentally activate thecortical networks using various techniques. Specifically, paired associative stimulation(PAS) combines single-pulse transcranial magnetic stimulation (TMS) of the primary
motor cortex (M1) and single-pulse electrical stimulation of the periphery to activate theafferent sensory circuits (for a review, see [27, 136]).
Using PAS, repeated cortical and peripheral stimuli, delivered at specific inter-stimulus intervals, can elicit long-term potentiation (LTP)- and long-term depression (LTD)like plasticity using Hebbian rules of associative learning [136]. Specifically, it has beenshown that mechanisms of PAS follow spike timing-dependant plasticity (STDP) (for areview, see [94]), where synaptic efficacy and polarity are determined by the temporalsequencing of pre- and postsynaptic terminal activity [24, 141]. Generally, facilitationof synaptic efficacy can occur if a presynaptic neuron fires before the postsynaptic neuron [40], while inhibition can occur if postsynaptic activations proceed or occur without
presynaptic activation [16, 73]. Adherence to STDP rules during PAS was shown to be

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functionally relevant for increasing voluntary motor output [141], which can serve as atherapeutic tool to enhance recovery after injuries [24].
It has also been suggested that different forms of associative stimulation do not necessarily need to adhere to the STDP rules to achieve synaptic efficacy [26], with multiple possible pathways available to induce corticospinal neuroplasticity within the
CNS [27]. For instance, when associative stimulation is applied with continuous trainsof peripheral electrical stimulation, such as during FES, it is also possible to facilitate
corticospinal excitability (e.g., [28, 123]). Similarly, repetitive TMS (rTMS) delivery ofintermittent theta burst stimulation (iTBS) over the primary motor cortex can drive corticospinal excitability [54] and it was also shown to facilitate corticospinal excitability if
applied before, but not after, electrical stimulation of the periphery [154]. Another formof non-invasive brain stimulation that could be effective for facilitating the cortical networks involves transcranial direct current stimulation (tDCS). When tDCS was applied
over the contralateral M1 simultaneously as the medial nerve stimulation, corticospinal excitability facilitation was larger compared to when tDCS was applied alone, whileelectrical stimulation alone delivered with sham tDCS did not produce an effect [125].
Importance of associative form of stimulation is emphasized by the fact that stimulation of the periphery was generally less effective in facilitating corticospinal excitability when delivered alone. These findings show importance of brain state dependence forobtaining optimal effects when using non-invasive brain stimulation [55] and associative activation of muscles and nerves. Further evidence of cortical state dependency on
corticospinal neuroplasticity has been illustrated through oscillatory beta cycle associative stimulation [63]. Details of non-invasive brain stimulation techniques are summarized in comprehensive reviews elsewhere (e.g., [55]). Overall, it is clear that multiple
mechanisms likely determine the role of corticospinal neuroplasticity during associative
stimulation, with modes of delivery of both cortical and peripheral stimulation playing a
crucial function. While non-invasive brain stimulation techniques can undoubtedly offer
important neurophysiological insights into associative stimulation mechanisms, which
can be proven to be clinically relevant, the objective of the following section is to investigate how descending voluntarily commands can be synchronized with activation of
peripheral activation of muscles and nerves.
Contractions that mimic voluntary-like movements were suggested to be importantfor generating cortical facilitation [26]. However, these effects are likely due to peripheral
afferent feedback delivery through cutaneous and muscle spindle activation [151], which
can also be applied by properly adjusting the stimulating parameters [13, 31]. Whileelectrical stimulation alone offers a means of artificially producing muscle contractions
by peripheral (direct) activation of the muscles without the central (cortical) drive, its
effectiveness may be enhanced through voluntary contractions to a greater extent compared to electrical stimulation alone [35]. For instance, it was shown that delivery ofelectrical stimulation at the onset of muscle electromyographic (EMG) activity during
wrist extension was successful in facilitating corticospinal MEP responses, while electrical stimulation alone was not [142]. Voluntary activations and electrical stimulation
can also induce reciprocal changes in corticospinal excitability in agonist and antagonist
muscles [155]. Using fMRI, the magnitude of cortical activation changes relative to rest
were shown to be larger during voluntary contractions of upper-limb muscles compared

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to FES-induced movements in the M1, S1, and SMA areas [57]. However, combined
voluntary and FES-induced contractions produced larger activations in the M1 and S1
areas compared to FES alone [57]. Although these studies suggest that voluntary activations can provide additional benefits compared to electrical stimulation of muscle andnerves alone, recent controlled trials investigating cyclic FES (i.e., without voluntary
drive) and EMG-triggered FES (i.e., with voluntary drive) concluded that functional benefits may not be different between the two modes of delivery in stroke patients [90, 156].
On the practical level, individuals with neurological impairments may not always be able
to generate sufficient or correct sequence of voluntary muscle contractions to reinforce
electrical stimulation delivery.
Motor imagery can also offer a means to activate the cortical circuits during electrical stimulation of muscles and nerves. This strategy, in which patients imagine the precise execution of movements without any overt movement, is believed to engage similar
neural networks as those involved in the actual production of movement [25]. It was
shown recently that combined delivery of FES and motor imagery resulted in stronger
cortical desynchronization compared to FES alone and motor imagery applied prior todelivery of FES [121]. When motor imagery was provided by means of watching andimagining actions shown on a pre-recorded video of grasping, while not producing the
movements, it was shown that concurrent electrical stimulation facilitated MEP corticospinal excitability and that either motor imagery and electrical stimulation alone did
not elicit any effects [157]. Similar acute effects were shown using combined motor
imagery and electrical stimulation of the lower-limbs [137]. Preliminary results withchronic stroke patients also suggest that applying electrical stimulation in combination with motor imagery over the course of 10 days may possibly improve upper-limb
function after the intervention cessation [106]. Tasks involving motor imagery and/or
action observation can produce reliable and muscle-specific excitability of corticospinal
responses in the upper-limbs (e.g., [39, 138]). Moreover, meta-analyses of a large body ofliterature investigating brain activations during motor imagery tasks concluded that thevoluntary movements, action observation (visual display of tasks), and motor imagery
alone (without the visual display) can all consistently give rise to activations in the premotor, parietal, and somatosensory cortical areas [51, 129]. Using motor imagery tasks,
even without concurrent electrical stimulation, can therefore have numerous benefits inrehabilitation [135]. Considering associative stimulation, it is also of particular relevancethat similar cortical areas activated by motor imagery are also recruited by electricalstimulation of muscles and nerves [26]. A practical consideration of motor imagery isthat cortical activations are not necessarily ensured nor synchronized with the deliveryof electrical stimulation. It is also well known that ability to produce motor imagery is
subject-specific and that not all individuals can produce consistent brain activity withsame effectiveness [77].
On the other hand, motor imagery can give rise to brain activity which can be detectedusing non-invasive brain activity recordings in real-time, i.e., electroencephalography(EEG). Such motor imagery-based phenomena typically include event related desynchronization (ERD) of EEG oscillatory cortical activity, which can be used in BCI applications to provide feedback to the users or control external devices [77, 138]. Single-trial
movement-related cortical potentials (MRCP) is another movement-related EEG activity

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that can predict movement onset without actual motor activity [101, 102]. Regardless, ofthe method for producing movement-related brain activity (motor execution or imagination with and/or without visual cues), such approaches can be used to trigger a BCI
system to control external devices. These serve as a basis for BCI-controlled FES systems, which can be used to activate electrical stimulation to ensure that cortical andperipheral stimulations are synchronized [91, 92]. A discussion about BCI-controlled
FES associative stimulation follows. The primary focus of this work will be on upperlimbs, while there is existing an important body of literature examining lower-limb control using BCI associative stimulation (e.g., [91, 92]).

BCI control for stimulation of muscles and nerves

Original BCI systems were developed to translate brain signals for the purpose of communication or control of artificial orthoses [152]. However, recent applications include
replacing, restoring, enhancing, supplementing, or improving the natural outputs produced by the CNS [152]. Here, we will focus on the use of BCI systems to improve natural motor control through guiding activity-dependant plasticity that may be able torestore natural movements after neurological injuries. Use of BCI in rehabilitation forthe purpose of improving motor function has gained considerable attention recently,
with various applications summarized in comprehensive reviews elsewhere [20, 30, 72,112]. For instance, a recent randomized trial used BCI to guide motor imagery during rehabilitation after stroke [113]. Compared to the control group, which performed
motor imagery without feedback, the BCI group had greater functional gains after thetherapy, suggesting that motor imagery-based feedback can also be used in rehabilitation [113]. Pairing motor commands from the BCI with the correct sequence of movements using a robotic orthosis can also result in cortical facilitation [49] and improved
motor function after stroke [4, 120]. Specifically, a clinical trial also showed that using
a BCI-controlled hand-arm orthosis immediately before the physical therapy session
was more effective for improving upper-limb function compared to the control groupwhich received randomly triggered orthosis before the therapy [120]. Similarly, using
BCI-based robotic intervention was faster to improve upper-limb function after stroke
compared to the control group which did not use the BCI system [4]. These studies agree
that BCI can be used as a priming intervention to facilitate excitability of the sensorimotor cortical networks which can maximize the effects of subsequent therapy. Moreover,these studies suggest that that BCI systems can be used to facilitate associative Hebbianlearning by pairing cortical activation with effective feedback using robotic orthosis or
motor imagery to drive CNS neuroplasticity.
Considering the neurophysiological effects of electrical stimulation of muscle andnerve, which were presented in earlier part of this review (see Sect. “Neurophysiological effects”), BCI-controlled FES can also be viewed as a form of associative intervention
that can be even more effective in facilitating feedback to the CNS to induce neuroplasticity and improve motor function. Indeed, in able-bodied people, BCI-FES systems wereshown as more effective compared to motor imagery as feedback [15]. Use of BCI-FES
was also shown to be effective for facilitating corticospinal excitability after short-term
interventions [82]. A brief overview of the architecture of BCI-controlled FES systems

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will be presented next, followed by a review of clinical applications of BCI-FES for rehabilitation of upper-limb motor function as well as a summary of the proposed neurophysiological mechanisms of their action.
Architecture of BCI‑FES systems

The proposed system architecture of typical BCI-controlled FES systems (e.g., [17, 34,
80,93, 97, 107]) is shown in Fig. 1b. Overall, BCI systems were mainly used as an EEGtrigger (i.e., brain switch) for activation of a pre-programmed FES neuroprosthesis.
Almost all systems utilized a binary (one degree-of-freedom) control to detect: (i) rest;and (ii) active (movement) states. Although hand kinematic information [99] and evenfingertip trajectories [98] can be decoded from cortical signals, these typically require
intracranial electrodes and remain unfeasible for non-invasive BCI applications. Noninvasive brain recordings were typically obtained over the sensorimotor cortical areasusing EEG signals, which were amplified and recorded through standard configuration
procedures, with special precautions to avoid recording physiological or other artifacts(for a review of EEG signal acquisition, see [95]). During the acquisition stage, signals are
typically band-pass filtered in the range of approximately 1 to 40 Hz, where a significant
portion of the cortical oscillation signal power originates [110, 97, 95]. With little or no
additional processing applied, signals are then recorded, and additional processing steps
can be applied digitally.
Operation of the system is divided into two steps: (1) calibration of the state decoder(classification of rest or active states), which is performed offline; and (2) control of FES
system in the real-time (online) (Fig. 1b). During the calibration stage, motor imagerybased tasks are presented using a visual display with the subjects relaxed and/or during
motor execution attempt [34], while recording synchronous (or cue-based) EEG activity.
The objective of this offline step is to select a combination of electrode sites and frequency bands that would be used for online control. The classifiers typically detect event
related desynchronization (ERD) of brain oscillatory activity [58], which is typically present before motor tasks [44]. During rest, brain activity in the sensorimotor areas can becharacterized by synchronous oscillatory patterns, while prior to the movement onset
or before attempted or imagined movements, desynchronization (ERD) of specific frequency bands occurs [111]. Overall, ERD can be a reliable biomarker for detecting motor
cortical activity using EEG recordings, and it has been shown to reflect excitability ofthe primary motor cortex [139] and spinal motoneurons [140]. Considering that ERD
frequency characteristics are subject-specific [111], a common procedure is to plot thetime–frequency signal power to help manually identify the ERD frequency bands thatwill be used for online control for each participant (e.g., [34, 58, 80, 97]). This emphasized the necessity of the calibration step, while re-calibration is commonly required
prior to each training session. Alternatively, signals can be subdivided into typical frequency bands: (a) alpha band (8–13 Hz), also know as mu band in the sensorimotorarea, which is typically associated with restfulness states; and (b) beta band (13–30 Hz),which is associated with various active concentration tasks, attention, or excitement.
While frequency bands can be adjusted based on various neurobiological considerations[95], typically mu and beta bands were used in most BCI-FES applications. The power ofthese pre-determined frequency bands or other spatiotemporal features of the signal canPage 15 of 30

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then be fed into a multi-feature linear discriminant analysis (LDA) classifier [93, 107] orother machine learning-based methods [17, 74]. In case of machine learning, the signals
are typically log transformed to normalize the data [107]. Similarly, electrode location(s)that will be used for online control of the BCI system can be selected manually from themost discriminant (e.g., [34]) or partially from a set of relevant candidate location andfed into an LDA classifier (e.g., [93]). This can result in several locations [93] or a single
EEG channel [58, 80] used as a BCI control signal. An alternative, approach is to use
spatial filtering technique instead of manual selection for EEG source localization. Suchspatial filters in BCI applications includes common spatial pattern (CSP), large Laplacianspatial filter (LLSF) and optimized spatial filter (OSF) [101, 102]. Spatial filters have been
applied in BCI-controlled FES systems [17]. However, manual selection of features wasalways considered to account for discriminatory and prior neurobiological knowledgeof the features used for BCI control [17, 58, 80, 93]. Once the feature space is selected,
a set of “rules”, in terms of discriminant power of the EEG signals recordings, is determined based on the machine learning algorithm [17, 93, 107] or as a simple signal power
threshold (e.g., [34]). Typically, in the online operation mode, these rules can be updated
automatically based on a running average performance [34, 107] or by the experimenter[80] to adjust for the slow signal drifting. Thus, BCI-FES systems operation depends
on the effective collaboration of two adaptive controllers, the BCI system and the brain[152], which may prove to be critical for inducing neuroplasticity.
To control the FES neuroprosthetic in real-time, the EEG-trigger brain switch can be
operated using asynchronous (non-cued) mode or simple synchronous (e.g., GO cue)configuration using a visual display [96]. Typically, the user attempts a movement over a
period of time, while the algorithm detects the state of the decoder (e.g., rest or active) ina fixed timeframe, after which FES delivers a corresponding pre-programmed sequenceof muscle activations. If the algorithm can not detect a change in the state, either theexperimenter can provide manual control (e.g., [58, 80]) or the trial be considered a “nodecision” [17]. In addition to physiological and other artifacts, an inherent problem of
BCI-controlled FES systems is that the recorded EEG activity is noisy during activationof FES. Several artifact reduction signal processing techniques have been compared withan intracortical BCI-FES system [158]. Linear regression referencing (LRR), which creates channel-specific reference comprised of the weighted sums of other channels by
assuming that artifact is similar across channels, was shown as superior compared toother method such as common average referencing and blanking methods [158]. However, most non-invasive systems in rehabilitation utilize a brain switch BCI, whereby
EEG recording is turned off after the decoding, while FES is applied. This can limit theapplications of non-invasive BCI-FES to synchronous (cue-based) operation. While noncued (asynchronous) BCI are desirable for prosthetic applications to restore naturalfunction, rehabilitation applications aim to improve voluntary motor function through
BCI-FES training. Specifically, the goal of BCI-FES rehabilitation is to facilitate associative Hebbian learning by pairing cortical activation with FES, which can effectively be
accomplished using cue-based BCI operation. Overall, complexity of the BCI-FES system should be balanced to consider accuracy as well as practical considerations requiredfor clinical implementation. These considerations present some general system architecture of the existing BCI-FES systems.

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Examples of BCI‑FES for restoration of upper‑limb motor function

A summary of the reviewed non-invasive BCI-controlled FES literature in the field of
motor rehabilitation is presented in Table 1. Most clinical applications of BCI-FES inrehabilitation has been performed in stroke patients and a majority of these are single-subject case studies [34, 58, 80, 93]. This body of literature has provided vital data
regarding feasibility of clinical implementation and hypotheses related to mechanismsof recovery. Notably, Daly and colleagues [34] used a BCI-system to control FES tocontrol voluntary finger function in an individual who lost voluntary upper-limb control as a result of a stroke sustained 10 months prior to the study. Using an ERD signal
power threshold-based method to detect a change of the beta frequency band oscillations in the sensorimotor area, the BCI system triggered finger extension and rest
states via FES in the contralateral hand. After only nine sessions, the participant’s ability to produce individual finger movements voluntarily was improved [34]. Moreover,in a series of case studies, Marquez-Chin and colleagues [58, 80] showed that a single
channel power change either in the beta [80] or mu oscillations [58] recorded over thesensorimotor area (note: specific location was adjusted during calibration for each participant) could be used as a threshold-based EEG-trigger to control FES effectively forfacilitating reaching and grasping. Although most BCI-FES studies were used to generate single joint movements, functional task performance during therapy [80], as shownin Fig. 1b, is relevant to induce activity-dependant plasticity. Using such a system, they
showed that 40 one-hour sessions induced meaningful clinical improvements indicating upper-limb recovery and functional independence improvements [58, 80]. Anotherclinical case study with a hemiplegia patient by [93] used a BCI system to detect finger
extension or rest states through an LDA classifier that was utilized to discriminate ERD
power changes of a 4-dimensional feature space recorded from EEG data (mu and beta
frequency bands of left and right hemisphere sensorimotor areas). In a single-subject
crossover design, the results of their study showed that BCI-FES use lead to marked
lateralization of cortical activations [93], compared to the initial assessments whichindicated diffuse fMRI activations of sensorimotor area. Moreover, changes in corticomuscular coherence were also shown in addition to the clinical improvements in theupper-limb function. Taken together, these studies present feasibility for clinical application of BCI-FES therapy (BCI-FEST) for improving motor function, which could also be
related to changes in the state of cortical sensorimotor networks.
Only recently larger studies using BCI-FES training with stroke patients have beenreported in the literature [17, 66, 74]. Specifically, in a randomized controlled trail,
Kim et al. [66] showed greater functional improvements using BCI-FES compared to
FES training alone. The authors presented evidence that training with BCI-FES fivetimes per week during a 4-week period could improve clinical scores associated withupper-limb functional recovery, while they did not present evidence to explain thepossible mechanisms or recovery. Another smaller trial with stroke patients by Li
et al. [74] also showed better functional recovery using BCI-FES, compared to theuse of FES alone, for upper-limb rehabilitation after stroke. Specifically, their results
showed motor functional improvements after 8 weeks of BCI-FES training, which
was also accompanied by activation of bilateral cerebral hemispheres, while activationof the affected sensorimotor cortex and parietal lobe were suggested to contribute

Page 17 of 30

1 stroke patient (M, 38 y)

15 stroke patients (BCI-FES: n = 8, Multi-channel EEG trigger using
5 M/3 F, 67.0 ± 5.0 y; control: n = 7,
an SVM classifier was used to
6 M/1 F, 67.1 ± 6.0 y)
detect rest vs. active states

30 stroke patients (BCI-FES: n = 15, 2-channel EEG trigger detected
6 M/9F; 59.1 ± 8.1 y; control: n = 15, attention-related sensory motor
6 M/9F, 59.9 ± 9.8 y)rhythm using a threshold toclassify rest vs. active states

1 stroke patient (M, 64 y)

Mukainoet al. [93]Li et al. [74]Kim et al. [66]Marquez-Chin
et al. [80]1-channel EEG trigger detectedby signal power change usinga threshold to classify rest vs.
active states

27 stroke patients (BCI-FES: n = 14, Multi-channel EEG trigger using a 1-channel FES was applied to
6 M/8F, 56.4 ± 9.9 y; control: n = 13, Gaussian classifier was used tofacilitate hand extension move‑
10 M/3F, 59.0 ± 12.4 y)discriminate rest vs. hand exten‑
mentssion states

BCI-FES therapy results in betterneurological recovery andimprovements in muscle strengthcompared to FES

Improvements in clinical scores aswell as the changes in arm func‑tion were shown after 40 sessions.

Improvements in functional mobil‑
ity and range of motion, suggest‑
ing improved motor function, wasshown after BCI-FES intervention.

Randomized controlled interven‑ Improvements in motor function
tion: (i) BCI-FES or (ii) FES were
were accompanied by increase indelivered two times per week forfunctional connectivity between
a period of 5 weeks (10 sessions
motor areas in the affected hemi‑
in total)sphere after BCI-FES

Randomized controlled interven‑tion: (i) BCI-FES or (ii) FES weredelivered 3–5 times weekly for
1 h (20 sessions in total)

Multi-channel FES facilitated
Case study intervention: 40 ses‑reaching movements: (i) forward
sions of 90 min of BCI-FES werereaching/retrieving (ii) reachingdelivered 3 times per week
to the mouth, and (iii) lateralreaching
Multi-channel FES was applied
to facilitate hand extension or
flexion of both hands during
active states

Clinical improvements and muscle
tone changes were seen after
BCI-FES as well as lateralization ofcortical activations and affectedcorticomuscular coherence

Participant’s ability to produce
voluntary finger movements was
improved after 9 sessions

Main results

Randomized controlled interven‑ Improvements in motor function,
activation of bilateral hemi‑tion: (i) BCI-FES or (ii) FES training
spheres, and altered activation of
was delivered three times per
week for 8 weeks
the sensorimotor cortexes wasshown after BCI-FES intervention.

Case study crossover control
design: (i) BCI-FES or (ii) FES wasdelivered for 60 min over thecourse of 2 weeks (10 days intotal)

Case study intervention: 9 sessionof 45 min delivered 3 times per
week for 3 weeks

Intervention

1-channel FES was applied to
Randomized controlled interven‑stimulate wrist extensor muscles
tion: (i) BCI-FES or (ii) conven‑of the affected upper-limb
tional therapy (control) was
delivered for 30 min per session
over 4 weeks

1-channel FES applied to facilitate
wrist extension movements

Multi-channel EEG trigger using
1-channel FES applied to facilitate
an LDA classifier detected signal
finger extension movements
power change of a multi-feature
space to classify rest vs. active
statesOsuagwu et al. 12 SCI patients (BCI-FES: n = 7; FES: Multi-channel EEG trigger using[107]
n = 5; 12 M, 51.7 ± 18.4 y)an LDA classifier detected signal
power changes of a feature
space to classify rest vs. active
statesBiasiucci et al.
[17]

FES

1-channel EEG trigger detected1-channel FES applied to facilitate
by signal power change using afinger extension movements
threshold method to classify rest
vs. active states

1 stroke patient (F, 43 y)

Daly et al. [34]BCI

Population

Study

Table 1 Summary of non-invasive BCI-controlled FES studies used for rehabilitation of upper-limb motor function

Milosevic et al. BioMed Eng OnLine(2020) 19:81
Page 18 of 30

1 stroke patient (M, 57 y)

Jovanovic
et al. [58]1-channel EEG trigger detectedby signal power change usinga threshold to classify rest vs.
active states

BCI
Multi-channel FES facilitatedfunctional movements: (i) hand
opening/closing, and (ii) arm
reaching/retrieving (varied
between sessions)

FES
Case study intervention: Two 40
one-hour BCI-FES sessions (80sessions in total) were delivered
with 3 sessions per week

Intervention

Improvements in clinical scores andfunctional capacity were shown
after completion of 80 therapy
sessions

Main results

BCI Brain-computer interface, EEG Electroencephalography, FES Functional electrical stimulation, LDA Linear discriminant analysis, SCI Spinal cord injury, SVM Support vector machine

Population

Study

Table 1 (continued)

Milosevic et al. BioMed Eng OnLine(2020) 19:81
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Milosevic et al. BioMed Eng OnLine(2020) 19:81

to function recovery [74]. The most comprehensive clinical trial in stroke patients
thus far was performed by Biasiucci and colleagues [17]. In a clinical study with 27
chronic stroke survivors, participants were divided into two groups to compare BCIcontrolled FES and FES alone interventions using otherwise comparable modes andintensities of delivery for stimulation of muscles. The BCI-controller was developed
using a machine learning approach with a number of physiologically relevant features
recorded from EEG signals over the sensorimotor areas of the affected hemisphere todetect the rest and active states targeting extension of the affected hand. The intervention was applied two times per week for a period of 5 weeks, while assessments
were performed before and after the intervention as well as in follow-up after theintervention period. The results of this trial showed that BCI-controlled FES groupexhibited clinically relevant and longer-lasting functional recovery results comparedto the FES group. Specifically, the BCI-group exhibited functional recovery profiles
which lasted 6–12 months after the invention, while increased functional connectivity between motor areas during voluntary hand contractions in the affected hemisphere were correlated to functional improvements [17]. Together, clinical trials instroke patients present evidence that functional motor improvements are associatedwith enhanced cortical activations in the affected hemisphere. Moreover, they all
agree that BCI-controlled FES is more effective in producing functional and corticalchanges compared to FES delivery alone.
Fewer BCI-FES applications have been implemented in individuals with SCI [78,96, 97, 107, 110]. Importantly, control of BCI and FES has been demonstrated in individuals with tetraplegia [110]. Most early applications of BCI were utilized to control an implanted upper-limb FES neuroprosthesis in patients with complete SCI [96,97]. Other detailed reports showed the efficacy of BCI with intracranial electrodes
to control FES [78]. These studies paved the way to the current research which uses
these two technologies as a therapeutic intervention. Specifically, a recent study by
Osuagwu and colleagues [107] applied BCI-FES as a rehabilitation intervention in
twelve subacute tetraplegic patients with incomplete injuries (C4-C7; ASIA B/C) who
were subdivided into BCI-FES and FES groups. The BCI controller was implemented
to detect desynchronization (ERD) of beta frequency EEG cortical oscillations using
an LDA classifier to discriminate between active and rests states. In the BCI-controlled group, FES was used to apply a sequence of hand extension and flexion tasks
during the active state or remain at rest in the rest state. The control intervention
group received an equivalent open-loop controlled FES (10 s ON/10 s OFF). Rangeof motion was improved in both groups, while muscle strength was observed in the
BCI group only after the intervention, compared to the pre-intervention assessments,
to suggest functional improvements. Initial assessments revealed that cortical desynchronization (ERD) during movement attempt was not focused in the sensorimotor area in both groups, while 20 sessions of BCI-FES intervention resulted in more
focused cortical EEG activity and remained widespread in the FES group [107]. It isrelevant to point out that the electrode locations for controlling the BCI-FES systemwere located over the sensorimotor cortices. Therefore, use of BCI-FES may be able
to promote re-organization after incomplete SCI by focusing associative activations
to a specific cortical area through a BCI system.

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Proposed mechanism of associative motor learning using BCI‑FES

Overall, most of the literature in stroke and incomplete SCI individual showed evidence
for improved motor function after using BCI-controlled FES system, compared to thecontrol conditions which usually involved FES delivery alone (e.g., [17, 107]). Evidence
from these studies also suggests that cortical level re-organization is correlated to functional recovery benefits. Specifically, results in stroke patients [17, 74, 93, 107] suggestthat improved sensorimotor activations in the affected hemisphere may be related tofunctional improvements. Intact motor areas topologically adjacent to the damaged site
within the primary motor cortex (M1) and other sensorimotor areas such as the premotor cortex (PM) and supplementary motor areas (SMA) in contralateral and ipsilateral hemisphere may assume control over the affected muscles via intricate intracorticalconnectivity networks after brain injury [104, 130, 150]. More focalized sensorimotor cortical activations were also reported after BCI-FES interventions in people withincomplete SCI [107]. BCI system can translate brain signals into a novel type of output[152]. Through such use- or activity-dependant associative stimulation, BCI-controlled
FES interventions may create new pathways for generating and transmitting neuronalcommands from the cortex to the muscles of interest.
So, how can BCI-controlled FES be used to effectively induce neuroplasticity in the
CNS? The likely mechanism for neurological basis for rehabilitation is that BCI can provide a way to modify neuronal activity with progressive practice that includes feedback
and reward [36]. Facilitation of motor recovery through error-based or reward-basedlearning most probably involves Hebbian-like plasticity [52], where a presynaptic input
onto a postsynaptic neuron is strengthened as a consequence of simultaneous activation
at the pre- and postsynaptic terminals. Cortical oscillatory desynchronization (ERD),
which has primarily been used as a trigger for BCI-controlled FES, was shown to reflect
excitability of the motor cortical [139] and spinal motoneuronal [140] networks. Similarly, electrical stimulation of muscles and nerves has been shown to activate cortical(e.g., [18]) and spinal motor networks (e.g., [53, 60]) during the stimulation. Therefore, apresynaptic input in the form of oscillatory cortical desynchronization which is detectedby the BCI system can generate strengthened connections due to simultaneous postsynaptic activation using FES activations of similar sensorimotor networks. The candidate
mechanism of such reward-based learning within the brain is probably upregulationof dopaminergic excitatory receptors and/or downregulation of GABAergic inhibitory
receptors [104]. Adherence to STDP rules can be relevant for increasing voluntary motor
output to enhance therapeutic outcomes [24,141]. However, it has also been suggested
that associative stimulation may not need to follow the strict timing principles [26]. As
indicated by the studies using rTMS [154] or BCI [113] to pre-activate (or prime) the
cortical sensorimotor networks before delivery of therapy, activations should at least
remain within some reasonable associative timeframe, while gains may be maximized by
optimizing delays between pre- and post-activations.
Evidence of adaptive cortical re-organization also exists. Specifically, more focal
cortical activations were observed after training using BCI-controlled systems thatutilized the same sensorimotor areas to control FES during the intervention [17,107]. Although prior neurobiological considerations were taken in most applications,electrode locations for controlling the BCI were chosen (either manually or throughPage 21 of 30

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machine learning) to include cortical areas that produced best discriminatory selectivity to maximize accuracy of the controller [17, 58, 80, 93]. Similarly, classifier
decoder was typically adaptively adjusted during operation to optimize performance[34, 80, 107]. Therefore, to achieve high reliability and accuracy, the system adapts
to the user behaviours. However, it must be kept in mind that BCI control requires
constant modification of two adaptive controllers, i.e., the decoding system as wellas the brain [152]. A recent elegant study in non-human primates, showed that BCIcontrolled FES can be used to induce adaptive cortical changes throughout different
sensorimotor cortical sites [61]. Specifically, through use of a BCI system, corticalactivity became localized around an arbitrarily selected cortical site that was used forcontrolling FES of upper-limb muscles in primates. The targeted cortical areas, which
included locations in the primary motor (M1), premotor (PM), and somatosensory(S1) cortex, could be reset and localized to a new site rapidly using BCI-FES training[61]. Although the evidence was shown in non-human primates using invasive techniques, this study provides important implications that BCI-FES system should balance adaptive control to guide neuroplasticity within specific cortical areas. Constantmodification of two adaptive controllers can, therefore, enhance CNS recovery.

Future trends in non‑invasive BCI‑controlled FES rehabilitation

Current evidence is in support of using non-invasive BCI-controlled FES for improving upper-limb motor function. Indeed, when FES systems were coupled with BCI,
the observed outcomes and cortical facilitation seemed to outperform FES alone after
stroke [17] and SCI [107]. Nonetheless, numerous issues remain to be resolved in the
future. Most current non-invasive BCI-FES applications use one degree-of-freedom
control to detect rest and active states, which can generate simple movements suchas wrist or finger extension (Table 1). Such goal based BCI operation that uses EEG asa trigger to activate a pre-programmed FES sequence may limit full potentials of thistechnology, despite the promising results so far. Future non-invasive BCI-controlled
FES systems should aim for continuous control of functional tasks which involve
several muscles such as during FEST interventions (e.g., reaching and grasping an
object). This has recently been demonstrated using in an individual with SCI using
an implanted upper-limb FES neuroprosthesis and intracortical electrodes [2]. Continuous control and decoding may also be advantageous in capturing rapid cortical
networks dynamics, which can be implemented in adaptive control. For non-invasive
systems, this will only become possible with the advancements in sensing technologies as well as improvements in neural decoding through use of machine learning orother algorithms capable of capturing complex cortical dynamics. The issue of FES
artifact removal from EEG recordings will also have to be resolved in the future before
continuous and asynchronous (non-cued) BCI-FES operation can be realized. In parallel, as the technology develops, understanding the underlying cortical re-organization both during BCI-FES operation and its interventional potentials through clinical
trials will also be critical for engineering neuroplasticity.

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Milosevic et al. BioMed Eng OnLine(2020) 19:81

Conclusions
Electrical stimulation of muscles and nerves can be used to generate muscle contractions and create functional movements of limbs. The implication of the motor and sensory recruitment of muscles with electrical stimulation goes beyond simple contractionof muscles and creation of functional movements. There is growing evidence to suggest that electrical impulses, which traverse the spinal cord and ascend to the brain, caninduce short- and long-term neurophysiological changes in the CNS. These changes
are likely responsible for the therapeutic effects that have been demonstrated in clinicalstudies using FES therapy (FEST). Most clinical applications have focused on generating functional contractions. However, involvement of the sensory afferent information
during electrical stimulation is thought to be critical for modulating the CNS circuits.
Stimulation parameters, such as pulse amplitude and pulse duration, determine which
neural fibers will be recruited, and the frequency of stimulating wave determines therate at which the action potentials are depolarized. Recruitment of muscles and nervesusing such stimulation can facilitate excitability of spinal reflex circuits and cortical networks. Moreover, long-lasting changes in the CNS may be enhanced by synchronizationof cortical and peripheral activations through associative stimulation. Brain-controlled
technologies offer a way to synchronize descending cortical commands and successful
execution of the intended tasks using a FES, which can promote associative Hebbianlearning. Emerging clinical evidence indeed suggests that BCI-controlled FES is an effective rehabilitation intervention that can possibly outperform FES alone. Future BCI-FES
applications should aim to achieve continuous and functional task control (BCI-FEST)
while adaptively modifying the control dynamics based on underlying cortical re-organization to engineer neuroplasticity in the CNS and maximize recovery of motor function
in individuals with neurological injuries.
Abbreviations
BCI: Brain-computer interface; CNS: Central nervous system; CSP: Common spatial pattern; EEG: Electroencephalography;
EMG: Electromyography; ERD: Event-related desynchronization; FES: Functional electrical stimulation; FEST: Functional
electrical stimulation therapy; fMRI: Functional MRI; LDA: Linear discriminant analysis; LLSF: Large Laplacian spatial filter;
LRR: Linear regression reference; LTD: Long-term depression; LTP: Long-term potentiation; M1: Primary motor cortex;
MEP: Motor evoked potentials; MPS: Motor point stimulation; MRCP: Movement-related cortical potentials; MRI: Magnetic
resonance imaging; NMES: Neuromuscular electrical stimulation; OSF: Optimized spatial filter; PAS: Paired associative
stimulation; PM: Premotor area; PNS: Peripheral nerve stimulation; PR: Parietal area; tDCS: Transcranial direct current
stimulation; TMS: Transcranial magnetic stimulation; rTMS: Repetitive TMS; S1: Primary somatosensory area; S2: Secondary
somatosensory area; SCI: Spinal cord injury; SMA: Supplementary motor area; STDP: Spike timing-dependant plasticity;
SVM: Support vector machine.
Acknowledgements
Not applicable.
Authors’ contributions
MM, CMC, KM, TN, MH, MRP and KN designed the concept of this review and conceptualized the article; MM drafted
manuscript and prepared the figures; MM, CMC, KM, TN, MH, MRP and KN edited and revised the manuscript; All authors
read and approved the final manuscript.
Funding
This project was supported by the Grant-in-Aid for Scientific Research (KAKENHI) from the Japan Society for the Promo‑tion of Science (JSPS) (#18H04082, #18KK0272, #19K23606, and #20K19412) and the MEI Grant B from the Global Center
for Medical Engineering and Informatics at Osaka University.
Availability of data and materials
Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
Ethics approval and consent to participate
Not applicable.

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Milosevic et al. BioMed Eng OnLine(2020) 19:81

Consent for publication
Not applicable.
Competing interests
MRP is a shareholder in company the MyndTec Inc, which manifactures functional electrical stimulation devices. The
remaining authors declare that there are no known conflicts of interest associated with this publication.
Author details
1
Graduate School of Engineering Science, Department of Mechanical Science and Bioengineering, Osaka University,
1‑3 Machikaneyama‑cho, Toyonaka, Osaka 560‑8531, Japan. 2 Institute of Biomedical Engineering, University of Toronto,
164 College Street, Toronto, ON M5S 3G9, Canada. 3 KITE Research Institute, Toronto Rehabilitation Institute - University
Health Network, 520 Sutherland Drive, Toronto, ON M4G 3V9, Canada. 4 CRANIA, University Health Network & Universityof Toronto, 550 University Avenue, Toronto, ON M5G 2A2, Canada. 5 Department of Neurological Diagnosis and Restora‑tion, Graduate School of Medicine, Osaka University, 2‑2 Yamadaoka, Suita, Osaka 565‑0871, Japan. 6 Department of Life
Sciences, Graduate School of Arts and Sciences, University of Tokyo, 3‑8‑1 Komaba, Meguro, Tokyo 153‑8902, Japan.
Received: 1 February 2020 Accepted: 10 October 2020

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