C

Hr

Ia

b

U

h

•••

a

ARRAA

KRBSMBSP

C

h0

Journal of Neuroscience Methods 246 (2015) 22–29

Contents lists available at ScienceDirect

Journal of Neuroscience Methods

jo ur nal home p age: www.elsev ier .com/ locate / jneumeth

linical neuroscience

and-in-hand advances in biomedical engineering and sensorimotorestoration

olanda Pisottaa, David Perruchoudb, Silvio Iontab,∗

Neurological and Spinal Cord Injury Rehabilitation Department A and CaRMA Lab, IRCCS Fondazione S. Lucia, Rome, ItalyThe Laboratory for Investigative Neurophysiology (The LINE), Department of Radiology and Department of Clinical Neurosciences,niversity Hospital Center and University of Lausanne, Lausanne, Switzerland

i g h l i g h t s

Robotic devices to re-establish brain-periphery communication.Brain function recovery after technology-based peripheral interventions.Engineering solutions for the restoration of the sensory–motor loop.

r t i c l e i n f o

rticle history:eceived 11 December 2014eceived in revised form 26 February 2015ccepted 3 March 2015vailable online 10 March 2015

eywords:ehabilitationiomedical engineeringensoryotor

rainpinal corderipheral nervous system

a b s t r a c t

Background: Living in a multisensory world entails the continuous sensory processing of environmen-tal information in order to enact appropriate motor routines. The interaction between our body andour brain is the crucial factor for achieving such sensorimotor integration ability. Several clinical condi-tions dramatically affect the constant body-brain exchange, but the latest developments in biomedicalengineering provide promising solutions for overcoming this communication breakdown.New method: The ultimate technological developments succeeded in transforming neuronal electricalactivity into computational input for robotic devices, giving birth to the era of the so-called brain–machineinterfaces. Combining rehabilitation robotics and experimental neuroscience the rise of brain–machineinterfaces into clinical protocols provided the technological solution for bypassing the neural disconnec-tion and restore sensorimotor function.Results: Based on these advances, the recovery of sensorimotor functionality is progressively becoming aconcrete reality. However, despite the success of several recent techniques, some open issues still needto be addressed.Comparison with existing method(s): Typical interventions for sensorimotor deficits include pharma-ceutical treatments and manual/robotic assistance in passive movements. These procedures achievesymptoms relief but their applicability to more severe disconnection pathologies is limited (e.g. spinal

cord injury or amputation).Conclusions: Here we review how state-of-the-art solutions in biomedical engineering are continuouslyincreasing expectances in sensorimotor rehabilitation, as well as the current challenges especially withregards to the translation of the signals from brain–machine interfaces into sensory feedback and the incorporation of brain–machine interfaces into daily activities.

© 2015 Elsevier B.V. All rights reserved.

ontents

1. Introduction—Robotic assistance in rehabilitation . . . . . . . . . . . . . . . . . . . . . . . .2. Invasive and non-invasive BMIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Abbreviations: BMI, brain–machine interface; BCI, brain–computer interface; FES, fun∗ Corresponding author.

E-mail address: ionta.silvio@gmail.com (S. Ionta).URL: http://www.unil.ch/fenl/page99866 en.html (S. Ionta).

ttp://dx.doi.org/10.1016/j.jneumeth.2015.03.003165-0270/© 2015 Elsevier B.V. All rights reserved.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

ctional electrical stimulation; SCI, spinal cord injury.

I. Pisotta et al. / Journal of Neuroscience Methods 246 (2015) 22–29 23

3. Neuroprosthetic control through functional electrical stimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264. Classification and modeling techniques for neuroprosthetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265. Future perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28. . . . . .

1

bs(i1lidhl(saraiio

ttoechtncecroTdteslBceipt2phoirsgta

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. Introduction—Robotic assistance in rehabilitation

In industrialized countries, 2‰ of the inhabitants are affectedy stroke (Kolominsky-Rabas et al., 2001) and about 50% of theurvivors must deal with permanent sensorimotor dysfunctionsBelda-Lois et al., 2011a). With over ten thousand spinal cordnjury (SCI) accidents (in USA only; Nobunaga et al., 1999) and.5 million amputations (Ziegler-Graham et al., 2008) per year,

imb disconnection or loss are the highest risks in North Amer-ca. Leaving high-level cognitive functions relatively preserved, butetermining serious low-level sensorimotor impairments whicheavily undermine patients’ ability to act and sense the world, these

esions result in dramatic consequences on psychological wellbeingMillan et al., 2010). In paralyzed patients, especially tetraplegics,ome emerging procedures are based on surgical techniques, suchs the “tendon transfer” to regain the active wrist extension andeduce functional deficits (Friden and Gohritz, 2012). Despite itsdvantages in promoting movement independency even in almostmmobilized patients, the invasive nature of this approach renderst less likely transferable to daily life conditions with respect tother modern technological solutions.

The traditional strategy for overcoming sensorimotor disordersypically entails two main directions. On the one hand, pharmaceu-ical treatment is employed to intervene against pain and clonus,ften resulting in symptoms relief but with controversial long-termfficacy (e.g. Dashtipour and Pender, 2008). On the other hand, thelassic physiotherapy technique is focused on avoiding muscularypotrophy by the administration of passive movement sessionshrough manual interventions (del-Ama et al., 2012). These tech-iques show general benefits, but they are affected by limitationsoncerning task-specificity, complexity, time-consumption, andxpertise-dependency. In addition, they are not sufficient to pre-isely measure sensorimotor improvements (Dietz, 2009) and toigorously balance the amount of provided assistance as a functionf the patient’s continuous developments (Popovic et al., 2003).rying to substitute manual interventions, the latest technologicalevelopments brought important advances to automated rehabili-ation, with a specific focus on repeatability and quantification. Forxample in stroke patients, regular physiotherapeutic treatmentsuch as robotics-assisted training results in more accurate and long-asting recovery of sensorimotor functions (Waldner et al., 2009).ased on the concept that the repetition of task-specific movementsan improve motor recovery (Takahashi et al., 2008), from the initialfforts in implementing robot-assisted procedures for rehabilitat-ng exclusively manual movements, in recent years the focus hasrogressively spread over solutions for re-training movements ofhe whole arms and legs (Belda-Lois et al., 2011b; Takahashi et al.,008). In order to be suitable for repetition-based rehabilitationrocedures, robotic devices should respect the functionality of theuman body in terms of dimensions, degrees of freedom, rangef motion. Once these requirements are fulfilled, the robots can bentroduced in clinical settings, as is the case of the semi-exoskeletalobot “ARMin II” dedicated to arm therapy, able to fit different body

izes, and equipped with position and force sensors to preciselyuide patients’ performance (Staubli et al., 2009). Robotic assis-ance in rehabilitation is important not only for repeatability butlso for monitoring and evaluating the progresses in rehabilitation.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

In this vein, it can also benefit from the advances brought by virtualreality in re-training protocols. In particular, implementing virtualenvironments allows to precisely control movement parameterswhile keeping the complexity of living in a multisensory world,e.g. integrating visual, auditory, and tactile stimulation during thevirtual simulation of playing the piano (Adamovich et al., 2009).In summary, as demonstrated by their documented safety, toler-ance by patients, and validity (Burgar et al., 2011; Krebs et al.,1999), robotic devices can be valuable adjunctions to conventionalapproaches. However, despite some medical cases might benefitfrom automated manipulation (Prange et al., 2006), these tech-niques do not apply to more severe clinical conditions such ascomplete loss of limbs (e.g. amputation) or destruction of connec-tion between the central and peripheral nervous system (e.g. spinalcord injury).

How to treat these conditions? The end of the last century wasdistinguished by a revolutionary discovery that heavily impactedthe scientific and clinical research aimed at overcoming the impair-ments due to traumatic or degenerative sensorimotor loss. As theAustralian artist STELARC realized in his visionary live performance“The Third Hand” in 1980 (a robotic hand controlled by electromyo-graphic signals and incorporated into the user’s functionalities), theinteraction between cognitive neuroscience and biomedical engi-neering is essential for the development of prosthetics. About tenyears earlier staining techniques in primates showed that the elec-trical activity of the neurons can be used to trigger the movementof an external robotic device (Fetz and Finocchi, 1971; Fetz, 1969).This revolutionary breakthrough gave birth to a complete newfield of scientific research and clinical application: “Brain–MachineInterfaces” (BMIs). Despite the vast variety of BMI designs andapplications, most of them follow a similar principle (Fig. 1). Bio-logical signals carrying neural information are recorded eithercortically or peripherally and are fed into a computer, which usesa decoding algorithm to translate the brain signals into compu-tational commands. These latter are used to control an effector,such as a neuroprosthesis. Then this brain–BMI–brain loop is closedwhen the user observes, the device sends feedback, and the brainencodes the output of the system. In recent years, the achieve-ment brought by the implementation of BMIs into the interventionprotocols for treating several sensorimotor impairments, such ashemiparesis or spinal cord injury, generated great emphasis in bothclinicians and scientists.

The birth of BMIs demonstrated that the translation of neu-ral activity into computational signals was a concrete reality anda possible way of restoring lost motor functions by establish-ing a brain–device communication system (Wolpaw et al., 2000).Thus, the research on substitution of lost functions continuouslydeveloped and resulted in the production of “Brain–ComputerInterfaces” (BCIs): systems able to record and convert in real-time neuronal signals into computational input (Wolpaw et al.,2000). BCIs received enormous attention and are nowadaysimplemented in a wide range of applications, including neu-roprosthetic devices aimed at re-establishing lost sensorimotor

functions such as reaching, grasping, or even walking. How-ever, before the full implementation of BMI and BCI systems instandard rehabilitation protocols some important issues must besolved.

24 I. Pisotta et al. / Journal of Neuroscience Methods 246 (2015) 22–29

Fig. 1. The brain–BMI–brain loop. Schematic representation of the neuro-computational circuitry between the different possible techniques for acquiring neural signals (left),computational decoders (lower), and devices (right). First, in the “recording” phase the brain activity is recorded through invasive and non-invasive methods, resulting inthe transmission of raw signal to the next phase (left panel). Second, during the “decoder” phase the neural signals are decoded and classified by machine-learning adaptivea wer pac vice s

ttabnfiao

lgorithms, which generate control commands to be sent to the external tools (loontrol different kind of hardware and software devices (right panel). Finally the de

As outlined in Fig. 1, here we summarize the available data onhe origin and status of the most influential BMI approaches andhe directions of future work, with a particular focus on benefitsnd controversies of each technique, describing the innovationsrought by merging the advances in biomedical engineering and

eurological rehabilitation. In Section 1, we will compare bene-ts and limitations of invasive and non-invasive BMI systems, with

particular interest on customization and challenges (left panelf Fig. 1). In Section 2, we will illustrate how the developments

nel). Third, in the “effector” phase, the resulting computational output is used toends feedback signals back to the brain (upper arrow).

in BMIs paralleled the advances in clinical applications, includingadvanced techniques based on precise predictions of natural kine-matics as a function of non-invasively recorded patterns of brainactivity, as well as the restoration of motor functions by combiningmuscular stimulation with neuroprostheses (right panel of Fig. 1).

In Section 3, we will focus the role of neural modeling of kine-matics in the development of bio-mimetic controllers of roboticneuroprostheses (lower panel of Fig. 1). Finally, in Section 4, wewill address the current challenges for a full implementation of

oscien

neta

2

twdawitotnoniatttapatoiuHmvoa(crabfsomt

ibaavtnreqrciioTo

I. Pisotta et al. / Journal of Neur

europrosthetics in standard clinical procedures, especially thestablishment/restoration of sensory information from the pros-hesis in order to allow its fully compatible integration in dailyctivities (upper part of Fig. 1).

. Invasive and non-invasive BMIs

Nowadays, the ultimate advances in prosthetics allow amputeeso perform at the same or even higher level of the best athletes in theorld. However, the large scale diffusion of similar tools is limitedue to the lack of control and advantage of the device’s use in dailyctivities (Zlotolow and Kozin, 2012). Control, speed, power, andeight are only some of the parameters that have been improved

n contemporary prosthetics (Belter et al., 2013). However, whenhe only solution for restoring a motor function is the substitutionf the lost limb with an external device, one of the main issues ishe establishment of the connection between the device and theervous system. As a function of the used technique, the recordingf the neural activity can assume two different forms: invasive oron-invasive (Fig. 1). Fetz (1969) demonstrated that neural activ-

ty can be recorded by means of electrodes implanted in the brainnd translated in real-time into computational commands to con-rol a robotic device. Building on previous evidence demonstratinghe feasibility of implanting electrodes in alive brains for detec-ing e.g. epileptic seizures (electrocorticography, ECoG), and takingdvantage of the high quality of the recorded neural signals, thisioneering study represented a milestone for invasive BMI researchnd laid the foundations for a completely new field of research forhe next decades (e.g. Chapin et al., 1999). About half a centuryf animal research showed that there is no doubt about the valid-ty of the invasive BMIs approach for controlling external devicessing signals originating in the brain (Nicolelis and Lebedev, 2009).owever, despite the enormous effort in this direction, its imple-entation into clinical procedures is still very limited. Up to now

ery few single cases showed successful results, including the usef a computer cursor (Kennedy and Bakay, 1998), the actuation of

robotic hand (Hochberg et al., 2012, 2006), or a full robotic armZlotolow and Kozin, 2012). In a similar vein, intraneural electrodesan be implanted directly onto the peripheral nerves in order toecord neural signals from the spinal cord to the body peripherynd viceversa (Rossini et al., 2010). Using this technique it is possi-le to provide control of a prosthetic device as well as encode theeedback from the device and send it back to the central nervousystem (Di Pino et al., 2012). Nevertheless, the long-term efficacyf this approach is still under debate, due to the possible develop-ent of fibrotic tissue around the electrode, which would impair

he signal acquisition and recording.Beyond high quality signals, the main reasons for the limited

mpact of invasive BMIs on clinical practice include scarce num-er of usable channels (e.g. in Alivisatos et al., 2013), high risksssociated with infection or rejection (Lebedev and Nicolelis, 2006),nd anatomo-functional neural plasticity and cellular death. Con-ersely, non-invasive BMI can overcome similar issues, providinghe methodology for controlling external devices by means ofon-invasively recorded neural activity using electroencephalog-aphy (EEG), a cap of electrodes positioned on the scalp (Bortolet al., 2014). With respect to invasive BMI, the signals have loweruality and the system requires longer training. However, theisks of non-invasive BMI are lower, the quality of the signalan be improved by using adaptive algorithms during the train-ng (Wolpaw and McFarland, 2004), and its sensitivity renders

t the best BMI candidate to differentially classify a broad panelf conditions, including cognitive states (Johnson et al., 2011).hese characteristics promoted the developments of a multitudef EEG-based BMIs (Lebedev and Nicolelis, 2006), showing how

ce Methods 246 (2015) 22–29 25

this approach is useful to re-establish communication skills withthe environment. Historically, one of the first non-invasive BMIswas used to allow a patient suffering from multiple sclerosis to“write” words and sentences on a computer screen (Sutter, 1992).The system was able to detect and classify the differential pat-terns of neural activity in the primary visual cortex associatedwith the observation of different letters. Then this information wastransferred to an elaboration software that in turn decoded theinput and produced letters on the screen, allowing the patient tocompose string of sentences. Non-invasive BMIs have been imple-mented not only to recognize externally-induced specific patternsof brain activity, but also and more importantly to classify volun-tary regulation of the brain waves. Based on the discovery thatlow-frequency neural activity can be voluntary controlled throughtraining (Birbaumer et al., 1992), specifically designed EEG-basedBMI systems have increased the sense of control of the device andthe range of potentially re-achievable motor routines (Birbaumeret al., 1999). In particular, a similar system gave back to a locked-in patient the ability to control a computer cursor and thereforeimproved his communication skills and provided internet access(Birbaumer et al., 2000; Kubler et al., 1999). Similar advances incommunication skills through the control of a computer cursorthanks to a BCI system have been achieved by both paraplegic(Kubler et al., 2005) and tetraplegic patients (Piccione et al., 2006;Sellers and Donchin, 2006). In addition to the re-establishment ofcommunication and control skills, EEG-based non-invasive BMIscan be used to recover neural activity.

According to the “simulation theory” (Jeannerod, 2001), thecovert stage of every action is a representation of the future, whichincludes the direct goal of the action. Covert and overt stages thusrepresent a continuum, such that every overtly executed actionimplies the existence of a covert stage. Based on the possibilityto further investigate such covert stages, it has been demonstratedthat mental simulation of movements relies on partially overlap-ping brain network with respect to real execution (e.g. Ionta et al.,2010) and that sensorimotor versus visual mechanisms are selec-tively activated by mental spatial transformations of bodily images(Blanke et al., 2010). Building on this evidence, a recent studyshowed a better efficacy of a BCI session that couples brain activ-ity (mental simulation of manual actions) and visual feedback (thesame actions performed by a virtual hand) (Pichiorri et al., 2013).Thanks to this procedure, in a group of stroke survivors the pat-terns of neural interhemispheric connections was restored to levelscloser to healthy subjects than to patients who did not undergo thesame BCI training.

EEG is not the only available technique for non-invasive BMIs.In fact, when the peripheral neural signal is still available a validalternative is represented by electromyography (EMG). Being basedon the recording and encoding of the residual muscular activity,EMG-based systems are particularly feasible for partially paralyzedpatients (Williams and Kirsch, 2004) and mild amputees (Hargroveet al., 2013). These and similar patients can benefit from the eas-ier setup of the EMG-based BMI and can use it for controllingneuroprostheses and exoskeletons in daily life conditions morefrequently with respect to EEG-based systems (Light et al., 2002;Zecca et al., 2002). The combination of EEG- and EMG-based BMIsto create a hybrid system, is the most recent advance in neuropros-thetics and the most promising future direction of BMI research.Few examples of this line of work have been already brought tothe community, showing that these systems can counterbalancemuscular fatigue (Leeb et al., 2010) and be used for complex motorroutines such as controlling an exoskeleton for walking (Cheron

et al., 2012). Despite their potential large scale application for sev-eral clinical conditions, the effective impact of hybrid BMIs onthe restoration of brain activity or plasticity still needs furtherinvestigations.

2 oscien

3s

tsasaepo

ip2cshtgnmfofmspgas(

fesscmCFtilpiteciemcrFrsWIioNdpt

6 I. Pisotta et al. / Journal of Neur

. Neuroprosthetic control through functional electricaltimulation

The most frequent and disabling consequence of stroke or SCI ishe progressive loss of muscular activity, laying the foundations foreveral secondary complications such as cramps, osteoporosis, orrthritis. Functional electrical stimulation (FES) is one of the newesttrategies for defeating muscular atrophy even in the absence of theppropriate neural signal. With the exception of muscular degen-ration, FES can also be used as a preventive procedure in order toreserve the mobility of at-risk joints and to safeguard the propri-ceptive information flow.

One of the first FES systems employed implantable electrodesnserted directly in the muscular fibers to allow a hemiplegicatient to move a completely paralyzed limb again (Loeb et al.,001). Despite the limitations due to the presence of an externalontroller and power supply, this demonstration led the way forlowing down or even stopping muscular decay. These limitationsave then been solved by providing patients with internal con-rol of the muscle contractions. One possibility to achieve theseoals is to bypass the impaired neural connection and use the sig-al from a preserved muscle to trigger the contraction of an affecteduscle. The recording of the healthy muscular activity can be per-

ormed by electrodes implanted in the muscle itself or positionedn the surface of the corresponding skin. Both strategies can per-orm remarkably well. Using electrodes implanted in the chest

uscles, a FES system took advantage of the electromyographicignals to restore hand control in an otherwise paralyzed strokeatient (Knutson et al., 2012). Similarly, a FES motor training pro-ram succeeded to control muscular activity below the lesion of

SCI patient and to ameliorate hand functions by making use ofurface electrodes positioned on a set of muscles above the lesionFerguson et al., 1999).

FES systems can be used not only for transferring the signalrom a muscle to another but also, and more importantly, for re-stablishing a compromised brain–muscles connection. Employingurface or implanted electrodes to trigger muscular activity, FESystems can be combined with BCI to improve power and pre-ision motor control, as well as to restore fundamental dailyovements such as grasping and lifting different objects (Marquez-

hin et al., 2009). This evidence supports the reliability of theES–BCI joint approach and demonstrates that non-invasive solu-ions for restoring lost motor functions can be as effective asnvasive procedures (Millan et al., 2010). The feasibility and long-asting benefits of the FES–BCI joint approach has been recentlyroven by the restoration of precision and power movements

n a SCI patient presenting the lesion at the cervical level andherefore suffering from complete motor and sensory loss (Rohmt al., 2013). Thanks to its flexibility, FES–BCI can be benefi-ial for a wide range of clinical conditions, including neurologicmpairments due to disorders of both the central and the periph-ral nervous system. Indeed, FES–BCI devices can be shaped asotor substitution tools to recover mobility (e.g. after SCI), or

an assume the configuration of assistive neuroprostheses forehabilitation purposes (e.g. after stroke). The effectiveness ofES–BCI in rehabilitation procedures is demonstrated by the neu-oplastic changes at the cortical level detected both in SCI andtroke patients following specific FES training sessions (Daly and

olpaw, 2008; Dietz and Curt, 2006; Dimyan and Cohen, 2011).ndeed, FES-enriched sensory feedback may facilitate the decod-ng of movement intention, improving therefore the rehearsalf that movement in order to enhance patients’ performance.

owadays, we are assisting to an exponential increase of FES–BCIevices implementing other technologies such as virtual reality torovide feedback to the user (Merians et al., 2006) or robotic assis-ance to improve repeatability and monitoring (Alon et al., 2007).

ce Methods 246 (2015) 22–29

The ultimate FES-based neuroprosthetics aims at combining thebenefits of stabilizing the joints and stimulating the muscles at thesame time, starting a new generation of hybrid orthoses (Weberet al., 2011). In this vein, the new challenge in the production ofhome-activity oriented and non-stationary systems is the fusion ofactive exoskeletons (for joint support) with non-invasive FES (formuscular activity) (e.g. the “OrthoJacket”; Schill et al., 2011). How-ever, before implementing non-invasive FES systems in standardrehabilitation procedures, future research on non-invasive solu-tions will have to address the limitations due to low selectivity inmuscular stimulation, weakness in deep muscles’ activation, diffi-culty in movement repeatability, and pain. Conversely, invasive FESsystems will have to solve the risks of infection, rejection, neuralplasticity, and cellular death.

4. Classification and modeling techniques forneuroprosthetics

“Control” can be seen as the ability to achieve goals by actingon dynamical systems. In daily life situations we are able to inte-grate different sources of information (e.g. visual or acoustic) toaccurately guide movements. During this integrative process, ourbrain must manage nonlinearities, noise, delays, and external per-turbations. In other words it works as a proper controller. One ofthe clearest examples of the brain’s skills in control is hand mobil-ity. With an almost infinite number of different configurationsand functions, hand mobility likely involves complex brain con-trol. Indeed, the straightforward execution of highly skilled manualmovements can be severely impaired if the connection betweenthe central nervous system and the periphery is damaged (reviewin Mateo et al., 2015).

One possibility to overcome similar disconnection-relatedimpairments is to bypass the lesion itself and use the brain signalsto directly control an external device. In this vein, seminal studieson animal models of SCI demonstrated that, once the spinal lesionis bypassed, it is possible to establish a loop between neuronal fir-ing and sensory feedback (Fetz and Baker, 1973; Fetz and Finocchio,1975, 1971). Building on this work, several recent studies showedthat both higher- and lower-order mammals are able to learn howto control computer cursors (Serruya et al., 2002; Talwar et al.,2002; Taylor et al., 2002). Similarly, human (Schmidt, 1980) andanimal research (Chapin et al., 1999) showed that motor functionscan be re-established by using brain signals to control three-dimensional mechanic movements of robotic devices. Mimickingcurrent models of sensorimotor integration (Perruchoud et al.,2014), the main issue of modern BMI systems is the implementa-tion of an online prediction system of the movement outcomes interms of endpoint trajectories and speed (Carmena et al., 2003). Bymeans of cortically implanted electrodes, animal research showedthat BMI systems are able to accurately recognize specific pat-terns of neuronal firing associated with different movements (BenHamed et al., 2007). Such tight relationship between high-qualitybrain signals, sensitive BMIs, and skilled device control is valid notonly for simple movements but also for more complex and orga-nized motor routines. Modern BMI systems are able to detect andencode hand and finger movements in natural actions performedby monkeys implanted with intracortical electrodes (Artemiadiset al., 2007; Vargas-Irwin et al., 2010). Based on single-unit recor-ding from the primary motor cortex in awake monkeys, trainedBMIs can distinguish between the firing patterns relative to flexionand extension of individuated fingers and wrist, with an average

accuracy between 90% and 99% (Aggarwal et al., 2008). In humansthe use of intracortical electrode is inherently limited, howeverECoG-based BMIs demonstrated their reliability in detecting differ-ential local motor potentials and encoding the properties of single

oscien

fiK

dpttcocatttmwn

apmcpag2oace(t2n–iiotus–Itdi(oooafivi

5

dtsrtnb

I. Pisotta et al. / Journal of Neur

nger movements and coordinated grasping (Acharya et al., 2010;ubanek et al., 2009).

Altogether, these findings show the ability of intracortical recor-ing to decode differential movement-specific neuronal firingatterns with high accuracy. However, chronically implanted elec-rodes are not a feasible solution in daily life conditions. Thusoday’s big challenge is to reach the same accuracy with lessonstraining and more flexible systems. In this vein, a new method-logy has been recently brought to the attention of the scientificommunity. By means of a non-invasive EEG-based system Agashend Contreras-Vidal (2011) used the signals recorded from the scalpo accurately encode spontaneous hand grasping in natural set-ing. This system was able to distinguish the different phases ofhe movement, including object selection, movement planning, and

otor execution. This is a proper example of how to use a systemith high accuracy to successfully decode hand kinematics throughon-invasively recorded neural signals.

Nowadays, this and similar approaches are bringing importantdvances toward the development of BMIs able to control com-lex devices such as multi-fingered hand prostheses. One of theost problematic aspects of such extremely complex BMIs, is the

apacity of dealing with non-linear signals. Our brain is used torocess non-linear information and to continuously update the rel-tive weight of the different sources of information in order touide adaptive behaviors (Ionta and Blanke, 2009; Ionta et al., 2007,013). As clearly proposed by current bio-computational modelingf sensory–motor integration, the brain is able to reciprocally bal-nce the incoming sensory information and the outgoing motorommand through inverse and forward internal predictions of thexpected motor outcome and the associated sensory consequencePerruchoud et al., 2014). Biomedical engineering is starting to facehis issue by developing adaptive robot controllers (Conforto et al.,009; Reinhart and Steil, 2009). In order to cope with the highlyon-linear relationship between sensory and motor information

which further depends on the degrees of freedom and anatom-cal constraints (Ionta et al., 2012) – one possible solution is themplementation of the so-called “inverse kinematics”, i.e. the usef kinematics equations to estimate the parameters to be appliedo a robotic device in order to reach a specific position and config-ration. Inverse kinematics translates the motion plan phase – thepecification of the motion sequence to reach the target position

into decomposed specific trajectories for each device’s segment.n this vein, a cortically-based artificial neural model able to adap-ively control an anthropomorphic virtual finger has been recentlyescribed (Gentili et al., 2012). Based on biological neural model-

ng of both cortical (Bullock et al., 1993) and cerebellar functionsContreras-Vidal et al., 1997; Porrill and Dean, 2007), the adaptationf inverse kinematics relies on the integration of different sourcesf sensorimotor information, including motor command, propri-ception, visual information, goal, and accuracy. Building on thedvantages of inverse kinematics, a recent cortically-based arti-cial neural model was able to adaptively control a human-likeirtual finger in order to accurately reach different targets emulat-ng human-like kinematics (Gentili et al., 2012).

. Future perspectives

One of the most challenging and at the same time necessaryevelopments of contemporary research in neuroprosthetics ishe effort to help users incorporate the prosthesis into the bodychema, i.e. the online sum of all the somatosensory information

elated to the body including e.g. proprioception, pain, interocep-ion, etc. (Berlucchi and Aglioti, 2010). Recent evidence in cognitiveeuroscience research showed the importance of the congruenceetween visual and tactile information for a proper functional

ce Methods 246 (2015) 22–29 27

representation of one’s own limb with respect to the rest of thebody (Ionta et al., 2013). Such a tight association between sen-sory processing and movement representation is demonstratedby clinical observations in patients suffering from the congeni-tal absence of a limb and developing unbalanced representationof their body (Funk and Brugger, 2008). This line of work sup-ports that an effective neuroprosthetic intervention cannot avoidtaking into consideration the afferent proprioceptive componentof any efferent motor command. The lack of sensory feedback inactual neuroprosthetics can explain why most of the users rejecttheir prosthetic limb despite its full control potentialities, prob-ably because their incomplete limb is still more functional andrealistic to them. Aiming at reproducing the original sensationsfrom a disconnected or lost limb, biomedical engineering is rapidlydeveloping in terms of attachment, control, and wearability. Therestoration of somatosensory information through the implemen-tation of sensory feedback in neuroprosthetics will restore thesensory–motor loop, increasing the chances of incorporating theprostheses into the body schema and therefore augmenting its usein daily life conditions. In this vein, it has been demonstrated theimportance of the integration between tactile feedback and rein-nervation (Kaczmarek et al., 1991; Marasco et al., 2009). In addition,despite its robustness in overcoming sensorimotor impairmentsdue to neural disconnection or physical loss (Bensmaia and Miller,2014), the methods based on cortically implanted devices haveto face all the contraindications associated with invasive BMIs.One way of implementing sensory feedback in future BMIs is tocombine novel algorithms able to convert neural activity into spe-cific motor commands (Shenoy and Carmena, 2014) and renderneuroprosthetics able to reflect the complex interaction betweendifferent mechanoreceptors determining the diversity of the cuta-neous experiences (Saal and Bensmaia, 2014).

In addition to the spatial component, an essential feature ofthe sensory feedback is the temporal link between the prostheticmovement and its associated feedback. Novel trends on the mosteffective procedures for a better brain-prosthetics integration focuson how to timely close the loop between sensory and motor infor-mation. Basic research showed that in order to properly incorporateall the body parts into a fully functioning body schema, the tim-ing of the information arising from different sensory modalities iscrucial (Botvinick and Cohen, 1998). Building on this evidence toneuroprosthetics research it has been shown that latencies up to300 ms between the prosthetic event and the perceived feedback,allow users to develop a proper sense of ownership and control ofthe prosthesis itself (Shimada et al., 2009). However, there is stillneed of specific training to establish a tight association betweenspecific sensory stimuli and states of the neuroprostheses (Antfolket al., 2013). Invasive procedure can be successful in restoring thetiming of sensory feedback (Berg et al., 2013; Tabot et al., 2013),allowing for example tactile exploration by means of active BMIs(O’Doherty et al., 2011). However, the methods to provide the brainwith information about timing of sensory information associatedwith actions still need to be ameliorated. BMI is a powerful multi-disciplinary approach to recover such communication breakdownand transfer information from the artificial actuators and to directlycontrol them via brain signals.

6. Conclusions

Our body is the main vehicle through which we interact withthe external world. The loss of motor function dramatically impacts

the continuous exchange between the mind’s goals and the body’sactuations. In recent years the research on BMI technology providedsolid solutions for overcoming such mind-body disconnection,holding promise to restore the interaction with the environment.

2 oscien

Hiiscaa

A

dfi

R

A

A

A

A

A

A

A

A

B

B

B

B

B

B

B

B

B

B

B

B

BB

B

8 I. Pisotta et al. / Journal of Neur

owever, as reviewed here, future research will have to solve somessues before BMI technology can enter everybody’s life, includ-ng the design of an easy and fully portable device to record brainignals, the development of adaptive algorithms to associate spe-ific brain activity patterns with specific intentions of the user,nd the restoration/replication of the somatosensation originallyssociated with each movement.

cknowledgements

This work was supported by the Swiss National Science Foun-ation (grant PZ00P1 148186 to Silvio Ionta). The proceduresollowed for selecting evidence and writing the manuscript werendependent from the funding source.

eferences

charya S, Fifer MS, Benz HL, Crone NE, Thakor NV. Electrocorticographic amplitudepredicts finger positions during slow grasping motions of the hand. J Neural Eng2010;7:046002.

damovich SV, Fluet GG, Mathai A, Qiu Q, Lewis J, Merians AS. Design of a complexvirtual reality simulation to train finger motion for persons with hemiparesis: aproof of concept study. J Neuroeng Rehabil 2009;6:28.

gashe HA, Contreras-Vidal JL. Reconstructing hand kinematics during reach tograsp movements from electroencephalographic signals. Conf Proc IEEE EngMed Biol Soc 2011;2011:5444–7.

ggarwal V, Acharya S, Tenore F, Shin HC, Etienne-Cummings R, Schieber MH, et al.Asynchronous decoding of dexterous finger movements using M1 neurons. IEEETrans Neural Syst Rehabil 2008;16:3–14.

livisatos AP, Chun M, Church GM, Deisseroth K, Donoghue JP, Greenspan RJ, et al.Neuroscience. The brain activity map. Science 2013;339:1284–5.

lon G, Levitt AF, McCarthy PA. Functional electrical stimulation enhancement ofupper extremity functional recovery during stroke rehabilitation: a pilot study.Neurorehabil Neural Repair 2007;21:207–15.

ntfolk C, D’Alonzo M, Rosen B, Lundborg G, Sebelius F, Cipriani C. Sensory feedbackin upper limb prosthetics. Expert Rev Med Devices 2013;10:45–54.

rtemiadis PK, Shakhnarovich G, Vargas-Irwin C, Donoghue JP, Black MJ. Decodinggrasp aperture from motor-cortical population activity. In: 2007 3rd interna-tional IEEE/EMBS conference on neural engineering; 2007. p. 518–21.

elda-Lois JM, Mena-del Horno S, Bermejo-Bosch I, Moreno JC, Pons JL, Farina D,et al. Rehabilitation of gait after stroke: a review towards a top-down approach.J Neuroeng Rehabil 2011a:8.

elda-Lois JM, Mena-del Horno S, Bermejo-Bosch I, Moreno JC, Pons JL, Farina D,et al. Rehabilitation of gait after stroke: a review towards a top-down approach.J Neuroeng Rehabil 2011b;8:66.

elter JT, Segil JL, Dollar AM, Weir RF. Mechanical design and performance spec-ifications of anthropomorphic prosthetic hands: a review. J Rehabil Res Dev2013;50:599–618.

en Hamed S, Schieber MH, Pouget A. Decoding M1 neurons during multiple fingermovements. J Neurophysiol 2007;98:327–33.

ensmaia SJ, Miller LE. Restoring sensorimotor function through intracortical inter-faces: progress and looming challenges. Nat Rev Neurosci 2014;15:313–25.

erg JA, Dammann JF 3rd, Tenore FV, Tabot GA, Boback JL, Manfredi LR, et al. Behav-ioral demonstration of a somatosensory neuroprosthesis. IEEE Trans Neural SystRehabil Eng 2013;21:500–7 (A publication of the IEEE Engineering in Medicineand Biology Society).

erlucchi G, Aglioti SM. The body in the brain revisited. Exp Brain Res2010;200:25–35.

irbaumer N, Roberts LE, Lutzenberger W, Rockstroh B, Elbert T. Area-specific self-regulation of slow cortical potentials on the sagittal midline and its effects onbehavior. Electroencephalogr Clin Neurophysiol 1992;84:353–61.

irbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kubler A, et al.A spelling device for the paralysed. Nature 1999;398:297–8.

irbaumer N, Kubler A, Ghanayim N, Hinterberger T, Perelmouter J, Kaiser J, et al. Thethought translation device (TTD) for completely paralyzed patients. IEEE TransRehabil Eng 2000;8:190–3 (A publication of the IEEE Engineering in Medicineand Biology Society).

lanke O, Ionta S, Fornari E, Mohr C, Maeder P. Mental imagery for full and upperhuman bodies: common right hemisphere activations and distinct extrastriateactivations. Brain Topogr 2010;23:321–32.

ortole M, Controzzi M, Pisotta I, Ubeda A. BMIs for motor rehabilitation: keyconcepts and challenges. In: Jose P, Diego T, editors. Emerging therapies inneurorehabilitation. Springer Berlin Heidelberg; 2014.

otvinick M, Cohen J. Rubber hands ‘feel’ touch that eyes see. Nature 1998;391:756.ullock D, Grossberg S, Guenther FH. A self-organizing neural model of motor equiv-

alent reaching and tool use by a multijoint arm. J Cogn Neurosci 1993;5:408–35.urgar CG, Lum PS, Scremin AM, Garber SL, Van der Loos HF, Kenney D, et al.

Robot-assisted upper-limb therapy in acute rehabilitation setting followingstroke: Department of Veterans Affairs multisite clinical trial. J Rehabil Res Dev2011;48:445–58.

ce Methods 246 (2015) 22–29

Carmena JM, Lebedev MA, Crist RE, O’Doherty JE, Santucci DM, Dimitrov DF, et al.Learning to control a brain–machine interface for reaching and grasping byprimates. PLoS Biol 2003;1:E42.

Chapin JK, Moxon KA, Markowitz RS, Nicolelis MAL. Real-time control of a robotarm using simultaneously recorded neurons in the motor cortex. Nat Neurosci1999;2:664–70.

Cheron G, Duvinage M, De Saedeleer C, Castermans T, Bengoetxea A, Petieau M, et al.From spinal central pattern generators to cortical network: integrated BCI forwalking rehabilitation. Neural Plast 2012:375148.

Conforto S, Bernabucci I, Severini G, Schmid M, D’Alessio T. Biologically inspiredmodelling for the control of upper limb movements: from concept studies tofuture applications. Front Neurorobot 2009;3:3.

Contreras-Vidal JL, Grossberg S, Bullock D. A neural model of cerebellar learn-ing for arm movement control: cortico-spino-cerebellar dynamics. Learn Mem1997;3:475–502.

Daly JJ, Wolpaw JR. Brain–computer interfaces in neurological rehabilitation. LancetNeurol 2008;7:1032–43.

Dashtipour K, Pender RA. Evidence for the effectiveness of botulinum toxin forwriter’s cramp. J Neural Transm 2008;115:653–6.

del-Ama AJ, Koutsou AD, Moreno JC, de-los-Reyes A, Gil-Agudo A, Pons JL. Reviewof hybrid exoskeletons to restore gait following spinal cord injury. J Rehabil ResDev 2012;49:497–514.

Di Pino G, Benvenuto A, Cavallo G, Denaro L, Denaro V, Rossini L, et al. In humanimplant of intraneural multielectrodes for controlling a 5-fingered hand pros-thesis and delivering sensorial feedback. In: B.S. Publishers, editor. Grasping thefuture: advances in powered upper limb prosthetics. Oak Park, USA: BenthamScience Publishers; 2012. p. 28–38.

Dietz V. Body weight supported gait training: from laboratory to clinical setting.Brain Res Bull 2009;78:I–VI (Reprinted from vol. 76, pg 459–463, 2008).

Dietz V, Curt A. Neurological aspects of spinal-cord repair: promises and challenges.Lancet Neurol 2006;5:688–94.

Dimyan MA, Cohen LG. Neuroplasticity in the context of motor rehabilitation afterstroke. Nat Rev Neurol 2011;7:76–85.

Ferguson KA, Polando G, Kobetic R, Triolo RJ, Marsolais EB. Walking with a hybridorthosis system. Spinal Cord 1999;37:800–4.

Fetz EB, Finocchi DV. Operant conditioning of specific patterns of neural and mus-cular activity. Science 1971;174:431–5.

Fetz EE. Operant conditioning of cortical unit activity. Science 1969;163:955–8.

Fetz EE, Baker MA. Operantly conditioned patterns on precentral unit activity andcorrelated responses in adjacent cells and contralateral muscles. J Neurophysiol1973;36:179–204.

Fetz EE, Finocchio DV. Correlations between activity of motor cortex cells andarm muscles during operantly conditioned-response patterns. Exp Brain Res1975;23:217–40.

Fetz EE, Finocchio DV. Operant conditioning of specific patterns of neural and mus-cular activity. Science 1971;174:431–5.

Friden J, Gohritz A. Brachialis-to-extensor carpi radialis longus selective nervetransfer to restore wrist extension in tetraplegia: case report. J Hand Surg Am2012;37:1606–8.

Funk M, Brugger P. Mental rotation of congenitally absent hands. J Int NeuropsycholSoc 2008;14:81–9.

Gentili RJ, Oh H, Molina J, Reggia JA, Contreras-Vidal JL. Cortex inspired model forinverse kinematics computation for a humanoid robotic finger. IEEE Eng MedBiol 2012:3052–5.

Hargrove LJ, Simon AM, Young AJ, Lipschutz RD, Finucane SB, Smith DG, et al. Roboticleg control with EMG decoding in an amputee with nerve transfers. N Engl J Med2013;369:1237–42.

Hochberg LR, Bacher D, Jarosiewicz B, Masse NY, Simeral JD, Vogel J, et al. Reach andgrasp by people with tetraplegia using a neurally controlled robotic arm. Nature2012;485:372–5.

Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, et al. Neuronalensemble control of prosthetic devices by a human with tetraplegia. Nature2006;442:164–71.

Ionta S, Blanke O. Differential influence of hands posture on mental rotation of handsand feet in left and right handers. Exp Brain Res 2009;195:207–17.

Ionta S, Ferretti A, Merla A, Tartaro A, Romani GL. Step-by-step: the effects of physicalpractice on the neural correlates of locomotion imagery revealed by fMRI. HumBrain Mapp 2010;31:694–702.

Ionta S, Fourkas AD, Fiorio M, Aglioti SM. The influence of hands posture on mentalrotation of hands and feet. Exp Brain Res 2007;183:1–7.

Ionta S, Perruchoud D, Draganski B, Blanke O. Body context and posture affect mentalimagery of hands. PLoS ONE 2012;7:e34382.

Ionta S, Sforza A, Funato M, Blanke O. Anatomically plausible illusory posture affectsmental rotation of body parts. Cogn Affect Behav Neurosci 2013;13:197–209.

Jeannerod M. Neural simulation of action: a unifying mechanism for motor cogni-tion. NeuroImage 2001;14:S103–9.

Johnson RR, Popovic DP, Olmstead RE, Stikic M, Levendowski DJ, Berka C. Drowsi-ness/alertness algorithm development and validation using synchronized EEGand cognitive performance to individualize a generalized model. Biol Psychol2011;87:241–50.

Kaczmarek KA, Webster JG, Bachyrita P, Tompkins WJ. Electrotactile and vibro-tactile displays for sensory substitution systems. IEEE Trans Bio-Med Eng1991;38:1–16.

Kennedy PR, Bakay RAE. Restoration of neural output from a paralyzed patient by adirect brain connection. NeuroReport 1998;9:1707–11.

oscien

K

K

K

K

K

K

L

L

L

L

M

M

M

M

M

N

N

O

P

P

P

P

P

P

I. Pisotta et al. / Journal of Neur

nutson JS, Chae J, Hart RL, Keith MW, Hoyen HA, Harley MY, et al. Implanted neu-roprosthesis for assisting arm and hand function after stroke: a case study. JRehabil Res Dev 2012;49:1505–15.

olominsky-Rabas PL, Weber M, Gefeller O, Neundoerfer B, Heuschmann PU. Epi-demiology of ischemic stroke subtypes according to TOAST criteria—incidence,recurrence, and long-term survival in ischemic stroke subtypes: a population-based study. Stroke 2001;32:2735–40.

rebs HI, Hogan N, Volpe BT, Aisen ML, Edelstein L, Diels C. Overview of clinical trialswith MIT-MANUS: a robot-aided neuro-rehabilitation facility. Technol HealthCare: Off J Eur Soc Eng Med 1999;7:419–23.

ubanek J, Miller KJ, Ojemann JG, Wolpaw JR, Schalk G. Decoding flexion of individualfingers using electrocorticographic signals in humans. J Neural Eng 2009:6.

ubler A, Kotchoubey B, Hinterberger T, Ghanayim N, Perelmouter J, Schauer M, et al.The thought translation device: a neurophysiological approach to communica-tion in total motor paralysis. Exp Brain Res 1999;124:223–32.

ubler A, Nijboer F, Mellinger J, Vaughan TM, Pawelzik H, Schalk G, et al. Patientswith ALS can use sensorimotor rhythms to operate a brain–computer interface.Neurology 2005;64:1775–7.

ebedev MA, Nicolelis MAL. Brain–machine interfaces: past, present and future.Trends Neurosc 2006;29:536–46.

eeb R, Sagha H, Chavarriaga R, Millan JD. Multimodal fusion of muscle and brainsignals for a hybrid-BCI. IEEE Eng Med Biol Soc 2010;1:4343–6.

ight CM, Chappell PH, Hudgins B, Engelhart K. Intelligent multifunction myoelectriccontrol of hand prostheses. J Med Eng Technol 2002;26:139–46.

oeb GE, Peck RA, Moore WH, Hood K. BION (TM) system for distributed neuralprosthetic interfaces. Med Eng Phys 2001;23:9–18.

arasco PD, Schultz AE, Kuiken TA. Sensory capacity of reinnervated skin after redi-rection of amputated upper limb nerves to the chest. Brain 2009;132:1441–8.

arquez-Chin C, Popovic MR, Cameron T, Lozano AM, Chen R. Control of a neu-roprosthesis for grasping using off-line classification of electrocorticographicsignals: case study. Spinal Cord 2009;47:802–8.

ateo S, Roby-Brami A, Reilly KT, Rossetti Y, Collet C, Rode G. Upper limb kinematicsafter cervical spinal cord injury: a review. J Neuroeng Rehabil 2015;12:9.

erians AS, Poizner H, Boian R, Burdea G, Adamovich S. Sensorimotor training ina virtual reality environment: does it improve functional recovery poststroke?Neurorehab Neural Repair 2006;20:252–67.

illan JD, Rupp R, Muller-Putz GR, Murray-Smith R, Giugliemma C, Tangermann M,et al. Combining brain–computer interfaces and assistive technologies: state-of-the-art and challenges. Front Neurosci 2010;4:1–15.

icolelis MAL, Lebedev MA. OPINION Principles of neural ensemble physiol-ogy underlying the operation of brain–machine interfaces. Nat Rev Neurosci2009;10:530–40.

obunaga AI, Go BK, Karunas RB. Recent demographic and injury trends in peopleserved by the model spinal cord injury care systems. Arch Phys Med Rehabil1999;80:1372–82.

’Doherty JE, Lebedev MA, Ifft PJ, Zhuang KZ, Shokur S, Bleuler H, et al. Active tactileexploration using a brain–machine–brain interface. Nature 2011;479:228–31.

erruchoud D, Murray MM, Lefebvre J, Ionta S. Focal dystonia and the sensory–motorintegrative loop for enacting (SMILE). Front Hum Neurosci 2014;8:458.

iccione F, Giorgi F, Tonin P, Priftis K, Giove S, Silvoni S, et al. P300-based braincomputer interface: reliability and performance in healthy and paralysed par-ticipants. Clin Neurophysiol 2006;117:531–7.

ichiorri F, Morone G, Iolanda P, Secci M, Cincotti F, Paolucci S, et al. BCI for strokerehabilitation: a randomized controlled trial of efficacy. In: Pons JL, Torricelli D,Pajaro M, editors. Converging clinical and engineering research on neuroreha-bilitation; 2013. p. 501–5.

opovic MB, Popovic DB, Sinkjaer T, Stefanovic A, Schwirtlich L. Clinical evaluationof functional electrical therapy in acute hemiplegic subjects. J Rehabil Res Dev2003;40:443–53.

orrill J, Dean P. Cerebellar motor learning: when is cortical plasticity not enough?PLoS Comput Biol 2007;3:1935–50.

range GB, Jannink MJA, Groothuis-Oudshoorn CGM, Hermens HJ, IJzerman MJ.Systematic review of the effect of robot-aided therapy on recovery of the hemi-paretic arm after stroke. J Rehabil Res Dev 2006;43:171–83.

ce Methods 246 (2015) 22–29 29

Reinhart R, Steil J. Reaching movement generation with a recurrent neural networkbased on learning inverse kinematics. Proc Conf Humanoid Rob 2009.

Rohm M, Schneiders M, Muller C, Kreilinger A, Kaiser V, Muller-Putz GR. Hybridbrain–computer interfaces and hybrid neuroprostheses for restoration of upperlimb functions in individuals with high-level spinal cord injury. Artif Intell Med2013;59(2):133–42.

Rossini PM, Micera S, Benvenuto A, Carpaneto J, Cavallo G, Citi L. Double nerve intra-neural interface implant on a human amputee for robotic hand control. ClinNeurophysiol 2010;121:777–83.

Saal HP, Bensmaia SJ. Touch is a team effort: interplay of submodalities in cutaneoussensibility. Trends Neurosci 2014;37:689–97.

Schill O, Wiegand R, Schmitz B, Matthies R, Eck U, Pylatiuk C. OrthoJacket: anactive FES-hybrid orthosis for the paralysed upper extremity. Biomed Tech2011;56:35–44.

Schmidt E. Single neuron recording from motor cortex as a possible source of signalsfor control of external devices. Ann Biomed Eng 1980;8:339–49.

Sellers EW, Donchin E. A P300-based brain–computer interface: initial tests by ALSpatients. Clin Neurophysiol 2006;117:538–48.

Serruya MD, Hatsopoulos NG, Paninski L, Fellows MR, Donoghue JP. Instant neuralcontrol of a movement signal. Nature 2002;416:141–2.

Shenoy KV, Carmena JM. Combining decoder design and neural adaptation inbrain–machine interfaces. Neuron 2014;84:665–80.

Shimada S, Fukuda K, Hiraki K. Rubber hand illusion under delayed visual feedback.PLoS ONE 2009;4:e6185.

Staubli P, Nef T, Klamroth-Marganska V, Riener R. Effects of intensive arm trainingwith the rehabilitation robot ARMin II in chronic stroke patients: four single-cases. J Neuroeng Rehabil 2009;6:46.

Sutter EE. The brain response interface—communication through visually-inducedelectrical brain responses. J Microcomput Appl 1992;15:31–45.

Tabot GA, Dammann JF, Berg JA, Tenore FV, Boback JL, Vogelstein RJ, et al. Restoringthe sense of touch with a prosthetic hand through a brain interface. Proc NatlAcad Sci USA 2013:2–7.

Takahashi CD, Der-Yeghiaian L, Le V, Motiwala RR, Cramer SC. Robot-based handmotor therapy after stroke. Brain 2008;131:425–37.

Talwar SK, Xu S, Hawley ES, Weiss SA, Moxon KA, Chapin JK. Rat navigation guidedby remote control. Nature 2002;417:37–8.

Taylor DM, Tillery SI, Schwartz AB. Direct cortical control of 3D neuroprostheticdevices. Science 2002;296:1829–32.

Vargas-Irwin CE, Shakhnarovich G, Yadollahpour P, Mislow JM, Black MJ, DonoghueJP. Decoding complete reach and grasp actions from local primary motor cortexpopulations. J Neurosci 2010;30:9659–69.

Waldner A, Tomelleri C, Hesse S. Transfer of scientific concepts to clinical practice:recent robot-assisted training studies. Funct Neurol 2009;24:173–7.

Weber DJ, London BM, Hokanson JA, Ayers CA, Gaunt RA, Torres RR, et al. Limb-state information encoded by peripheral and central somatosensory neurons:implications for an afferent interface. IEEE Trans Neural Syst Rehabil Eng2011;19:501–13 (A publication of the IEEE Engineering in Medicine and BiologySociety).

Williams M, Kirsch R. Evaluation of command sources for a high tetraplegia neuralprosthesis. Conf Proc IEEE Eng Med Biol Soc 2004;7:4803–6.

Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G,et al. Brain–computer interface technology: a review of the first internationalmeeting. IEEE Trans Rehabil Eng 2000;8:164–73 (A publication of the IEEE Engi-neering in Medicine and Biology Society).

Wolpaw JR, McFarland DJ. Control of a two-dimensional movement signal bya noninvasive brain–computer interface in humans. Proc Natl Acad Sci USA2004;101:17849–54.

Zecca M, Micera S, Carrozza MC, Dario P. Control of multifunctional prosthetichands by processing the electromyographic signal. Crit Rev Biomed Eng2002;30:459–85.

Ziegler-Graham K, MacKenzie EJ, Ephraim PL, Travison TG, Brookmeyer R. Estimatingthe prevalence of limb loss in the United States: 2005 to 2050. Arch Phys MedRehabil 2008;89:422–9.

Zlotolow DA, Kozin SH. Advances in upper extremity prosthetics. Hand Clin2012;28:587–93.