Towards Effective Non-Invasive Brain-Computer Interfaces Dedicated to Gait Rehabilitation Systems

Abstract

In the last few years, significant progress has been made in the field of walk rehabilitation. Motor cortex signals in bipedal monkeys have been interpreted to predict walk kinematics. Epidural electrical stimulation in rats and in one young paraplegic has been realized to partially restore motor control after spinal cord injury. However, these experimental trials are far from being applicable to all patients suffering from motor impairments. Therefore, it is thought that more simple rehabilitation systems are desirable in the meanwhile. The goal of this review is to describe and summarize the progress made in the development of non-invasive brain-computer interfaces dedicated to motor rehabilitation systems. In the first part, the main principles of human locomotion control are presented. The paper then focuses on the mechanisms of supra-spinal centers active during gait, including results from electroencephalography, functional brain imaging technologies [near-infrared spectroscopy (NIRS), functional magnetic resonance imaging (fMRI), positron-emission tomography (PET), single-photon emission-computed tomography (SPECT)] and invasive studies. The first brain-computer interface (BCI) applications to gait rehabilitation are then presented, with a discussion about the different strategies developed in the field. The challenges to raise for future systems are identified and discussed. Finally, we present some proposals to address these challenges, in order to contribute to the improvement of BCI for gait rehabilitation.

Full text

Brain Sci. 2014,4, 1-48; doi:10.3390/brainsci4010001
OPEN ACCESS
brain sciences
ISSN 2076-3425
www.mdpi.com/journal/brainsci
Review
Towards Effective Non-Invasive Brain-Computer Interfaces
Dedicated to Gait Rehabilitation Systems
Thierry Castermans 1,*, Matthieu Duvinage 1, Guy Cheron 2and Thierry Dutoit 1
1TCTS lab, Universit´
e de Mons, Place du Parc 20, Mons 7000, Belgium;
E-Mails: matthieu.duvinage@gmail.com (M.D.); Thierry.Dutoit@umons.ac.be (T.D.)
2LNMB lab, Universit´
e Libre de Bruxelles, Avenue Franklin Roosevelt 50, Bruxelles 1050, Belgium;
E-Mail: gcheron@ulb.ac.be
*Author to whom correspondence should be addressed; E-Mail: thierry.castermans@umons.ac.be;
Tel.: +32-65-37-47-37; Fax: +32-65-37-47-29.
Received: 2 October 2013; in revised form: 5 November 2013 / Accepted: 12 December 2013 /
Published: 31 December 2013
Abstract: In the last few years, significant progress has been made in the field of
walk rehabilitation. Motor cortex signals in bipedal monkeys have been interpreted
to predict walk kinematics. Epidural electrical stimulation in rats and in one young
paraplegic has been realized to partially restore motor control after spinal cord injury.
However, these experimental trials are far from being applicable to all patients suffering
from motor impairments. Therefore, it is thought that more simple rehabilitation
systems are desirable in the meanwhile. The goal of this review is to describe and
summarize the progress made in the development of non-invasive brain-computer interfaces
dedicated to motor rehabilitation systems. In the first part, the main principles of human
locomotion control are presented. The paper then focuses on the mechanisms of supra-spinal
centers active during gait, including results from electroencephalography, functional brain
imaging technologies [near-infrared spectroscopy (NIRS), functional magnetic resonance
imaging (fMRI), positron-emission tomography (PET), single-photon emission-computed
tomography (SPECT)] and invasive studies. The first brain-computer interface (BCI)
applications to gait rehabilitation are then presented, with a discussion about the different
strategies developed in the field. The challenges to raise for future systems are identified
and discussed. Finally, we present some proposals to address these challenges, in order to
contribute to the improvement of BCI for gait rehabilitation.

Brain Sci. 2014,42
Keywords: brain-computer interface; brain dynamics; brain imaging;
electroencephalography; rehabilitation; supra-spinal control of locomotion; walk
1. Introduction
More than 10 million people in the world live with some form of handicap caused by a central
nervous system (CNS) disorder. According to a recent Eurostat survey carried out in 25 European
countries, about 15% of the active population suffer from a long-term disability. This means that almost
45 million persons of working age, i.e., 15 to 64, live with such a medical condition. Disabilities affecting
mobility, in particular, often lead to exacerbated isolation and, thus, fewer communication opportunities,
resulting in a limited participation in social life. Encounters with other people are made difficult,
as well as simply performing usual daily tasks at home and, obviously, working. Lower limb disability
can have various origins, either medical (after a stroke, multiple sclerosis or Parkinson’s disease, for
instance) or accidental (road traffic accident, sport practice accident, etc.). In these conditions, either
leg muscles become inefficient for walking or the brain motor signals do not even properly reach the
spinal motoneurons commanding the leg muscles. The consequences are similar: the disabled person
cannot properly stand up or walk anymore. Developing technological tools to empower the lower limbs
of disabled people with walking ability will drastically change their day-to-day life, as they will perform
most usual daily activities more independently, both at home and outside, thus sustaining their own
inclusion in society.
In 2009, it was shown that invasive recordings of ensembles of cortical neurons in primary motor
and primary somatosensory cortices could be used to predict the kinematics of bipedal walking in
rhesus macaques [1]. The same year, another research team demonstrated that specific combinations of
serotonergic agonists and epidural electrical stimulation were able to restore the motor control of adult
rats after complete spinal cord transection [2]. More recently, epidural stimulations of the spinal cord
enabled a young paraplegic patient to achieve full weight-bearing standing, with assistance provided
only for balance, for more than 4 min. This technique also allowed him to produce locomotor-like
patterns and control some leg movements [3]. These recent breakthroughs are highly encouraging for
developing revolutionary rehabilitation strategies. However, the generalization of this type of clinical
rehabilitation is far from being applicable to all patients suffering from spinal cord injuries or other CNS
movement disorders.
The major challenge for walking rehabilitation in human arises from the fact that since the very first
step, the human CNS must dynamically integrate both conservative (postural stability) and destabilizing
(dynamic control of the body and limbs for forward progression) functions [4–7]. These two antagonistic
functions render the rehabilitation of human locomotion very challenging. This also explains why the
majority of leg prostheses available on the market are equipped with passive mechanisms. Although
these systems are functional, their performance is really limited compared to a real human leg, as they do
not have a self-propulsion capability. Therefore, amputees have to compensate for these limitations, and
they are generally faced with a reduced locomotion speed, a non-natural gait, considerable fatigue and,

Brain Sci. 2014,43
possibly, harmful consequences, like recurrent pain and injuries at the interface between their residual
limb and the prosthesis. Active prostheses solve these problems partially: powered by a battery-operated
motor, they move on their own and, therefore, reduce the fatigue of the amputees, while improving their
posture. Two main categories of active prostheses exist to date. Firstly, by analyzing the motion of
the healthy leg or the upper-body by means of sensors, the control system can identify the phase of the
gait cycle and trigger an actuator to appropriately adjust one or more prosthetic or orthotic joint [8–12].
The second type of active prostheses (or orthoses) is controlled by myoelectric signals recorded at the
surface of the skin, just above the muscles. These signals are then used to guide the movement of the
artificial limb [13–15].
Although the improvement brought by active prosthetic technology with respect to conventional
prostheses is indisputable, an intuitive interface from which users intent can be determined is still
missing. The purpose of this paper is to review, firstly, the substantial progress made in the understanding
of human locomotion control and, in the second part, the exploitation of this knowledge that is
being made in order to develop non-invasive brain-computer interfaces dedicated to walk rehabilitation
systems. Section 2summarizes the main mechanisms involved in human locomotion control. Section 3
focuses on the description of supra-spinal control of locomotion by summarizing the knowledge acquired
to date thanks to multiple methods of measuring neuronal activity. Section 4discusses different strategies
developed to produce walk rehabilitation systems driven by non-invasive brain-computer interfaces.
2. Deciphering Human Locomotion Control
Accumulating evidence suggests that human locomotion is actually based on a very complex
hierarchical system, which includes several control networks located both at the spinal and supra-spinal
levels. Basically, high-level motor commands are sent by the brain to a spinal network composed
of central pattern generators (CPGs), and at the same time, each level of motor control receives and
transmits peripheral sensory information (sensory feedback), which is used to modify the motor output
at that level. This section is first devoted to the description of each level of locomotor control, including
arguments supporting the existence of a CPG network and, simultaneously, the permanent action of
supra-spinal control. Then, the focus is on the spatial organization of supra-spinal control and its
temporal characteristics.
2.1. Description of the Gait Cycle
Human walking is composed of successive periodic and symmetric movements produced by a precise
sequence of collective actions, one leg alternating with the other one. The gait cycle is usually defined as
starting with the first contact (initial contact, or heel contact in normal gait) of one foot, so that the end
of the cycle occurs with the next contact of the same (ipsilateral) foot (see Figure 1). Each cycle begins
with a stance phase (when the foot hits the ground) and proceeds through a swing phase, until the cycle
ends with the limb’s next initial contact.
The stance phase of gait is divided into four periods: loading response, mid-stance, terminal stance
and preswing. The swing phase is divided into three periods: initial swing, mid-swing and terminal
swing. The beginning and ending of each period are defined by specific events, listed in Table 1. Figure 2

Brain Sci. 2014,44
presents the typical joint kinematics of the lower body during the gait cycle, for a range of walking
speeds [17]. Although the amplitude of the hip joint movement clearly increases as a function of the
walking speed, the movement pattern remains about the same, except at very slow walking speeds.
A similar behavior is found for the knee joint. Clear changes in amplitude and movement pattern occur
in the ankle joint, already at speeds slower than 3.0 km/h.
Figure 1. Illustration of different phases of the gait cycle. (A) New gait terms; (B) classic
gait terms; and (C) Percentage of gait cycle. Note: this figure is adapted with permission
from [16]; Copyright Demos Medical Publishing Inc., 2004.
Table 1. Subdivisions of the stance and swing phases of the gait cycle.
Phase Events
Stance
Loading response (0%–10%)
Begins with initial contact, the instant when the foot contacts the
ground. Normally, the heel contacts the ground first. The loading
response ends with the contralateral toe off, when the opposite extremity
leaves the ground. Thus, the loading response corresponds to the gait
cycle’s first period of double limb support.
Mid-stance (10%–30%)
Begins with the contralateral toe off and ends when the center of gravity
is directly over the reference foot. Note that this phase, and early
terminal stance, the phase discussed next, are the only times in the
gait cycle when the body’s center of gravity truly lies over the base
of support.
Terminal stance (30%–50%)
Begins when the center of gravity is over the supporting foot and ends
when the contralateral foot contacts the ground. During terminal stance,
the heel rises from the ground.
Preswing (50%–60%) Begins at the contralateral initial contact and ends at the toe off. Thus,
the preswing corresponds to the gait cycle’s second period of double
limb support.
Swing
Initial swing (60%–70%) Begins at toe off and continues until maximum knee flexion
(60 degrees).
Mid-swing (70%–80%) The period from maximum knee flexion until the tibia is vertical or
perpendicular to the ground.
Terminal swing (80%–100%) Begins where the tibia is vertical and ends at initial contact.

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Figure 2. Influence of walking speed on joint trajectories. Joint trajectories of the (A) hip,
(B) knee and (C) ankle joint at 10 different walking speeds (this figure is reproduced with
permission from [17]; Copyright IOS Press, 2010).
The stance phase lasts approximately 60% of the gait cycle, while the swing phase occurs during the
remaining 40% of the time. Each gait cycle includes two periods of double support, when both feet are
in contact with the ground. The first double support begins at initial contact and lasts for the first 10%
to 12% of the cycle. The second period of double limb support occurs in the final 10% to 12% of the
stance phase.

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2.2. Production of the Basic Locomotor Patterns: Arguments in Favor of a Human CPG Network
The rhythmic movements of the legs during stepping require a complex sequence of muscle
contractions to be executed by the lower limbs. The timing and level of activity of the numerous
muscles involved differ widely, as illustrated in Figure 3. The complex sequence of muscle contractions
is called the motor pattern for stepping. These patterns vary as a function of walking speed, the largest
differences occurring between 2.0 and 2.5 km/h for most muscles. A growing body of evidence suggests
that the patterns for stepping in mammals are produced at the spinal level by the central pattern generators
network [18].
The spinal central pattern generator network consists of coupled antagonist oscillators specifically
dedicated to extensor or flexor muscles acting at the different joints. This network generates the rhythm
and shapes the pattern of the motor bursts of motoneurons [19,20]. Their mechanism allows one
to produce simple and coordinated rhythmic movements, such as those involved in steady walking.
Numerous experiments with spinal cats (i.e., with complete transection of the spinal cord) have
demonstrated the presence of such CPG in lower mammals [20], and a similar conclusion has been
reached for primates [6]. Regarding humans, the evidence is only indirect [21,22].
The first argument is the fact that human infants exhibit a stepping behavior from birth [23] and even
before birth, as seen from ultrasound [24] or imaging recordings [25], although the brain has a weak
influence on the lower limbs movements at this early stage of development. Similar patterns are seen in
a variety of other immature mammals [26–28].
Furthermore, studies with young infants stepping on a split-belt treadmill have shown that the stepping
patterns in the two legs could be independent, but always remained coordinated (only one leg entering
the swing phase at a time), resulting in an integer relationship between the steps on each side. This
finding is in favor of the CPG hypothesis, because a stepping movement that would be due to a reflex
mechanism (e.g., such as the stretch reflex, a muscle contraction in response to stretching in the muscle)
would not exhibit such precise coordination. Other studies with babies have also shown that their
stepping mechanism responds to different perturbations the same way as the one of lower mammals,
for which the existence of CPG is almost certain [29]. Recently, this analogy has been verified on
the basis of experimental results consistent with the hypothesis that, despite substantial phylogenetic
distances and morphological differences, locomotion in several animal species is built starting from
common primitives, perhaps related to a common ancestral neural network [30].
Another argument supporting the theory of a human CPG comes from patients exhibiting involuntary
rhythmic spontaneous leg movements after both clinically complete [31,32] and incomplete [33] spinal
cord injury (SCI), thus with minimal influence of cortical signals. Similarly, sleep-related periodic leg
movements have been reported. These stereotyped, periodic, repetitive movements involve one or both
lower limbs. They consist of dorsiflexion of the ankle and toes and flexion of the hip and knee while
the subject is lying down or asleep [34,35]. The spinal origin of such movements is supported by their
presence in patients with complete spinal lesion.

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Figure 3. Influence of walking speed on electromyographic (EMG) activity patterns. EMG
activity patterns during different walking speeds of the (A) gluteus maximus (GL); (B) rectus
femoris (RF); (C) vastus lateralis (VL); (D) vastus medialis (VM); (E) lateral hamstrings
(HL); (F) medial hamstrings (HM); (G) tibialis anterior (TA); and (H) gastrocnemius
medialis (GM) muscles. EMG signals were normalized for each subject and each condition
by setting the difference between the lowest and highest EMG amplitude at 100% and
normalizing the curve according to this value (this figure is reproduced with permission
from [17]; Copyright IOS Press, 2010).
A final piece of evidence that CPG is at the basis of our rhythmic locomotor activity and can be located
in the spinal cord comes from experiments in which specific sites of the spinal cord were electrically

Brain Sci. 2014,48
stimulated. Indeed, it was shown that tonic electrical stimulation of the dorsal side of the spinal cord
could induce locomotor activity in intact, decerebrated and low spinalized cats [36–39]. A similar
spinal cord stimulation applied to persons with a complete spinal lesion eliciting a stepping activity
with reciprocal, organized EMG activity of symmetric muscles [21]. This suggests that a comparable
neural network (CPG) to that seen in the cat is present in humans.
2.3. Sensory Feedback Regulates the Stepping Patterns
Normal walking is generally considered an automatic movement. However, it is not necessarily
stereotyped. We constantly use sensory input to adjust stepping patterns to variations of the terrain
or to unexpected events. Three different types of sensory information are integrated to regulate our
way of stepping: somatosensory input from the receptors of muscles and skin, input from the vestibular
apparatus (balance control) and visual input [18].
Sensory feedback, elicited during gait, acts directly on the CPG and plays a major role in the phase
transitions during the step cycle [40]. In particular, it was shown that limb loading and hip position are
powerful signals for regulating the stepping pattern in human infants [41].
Cutaneous reflexes are also known to contribute to the correct execution of leg movements during
locomotion. They are largely under the control of the CPG. In this way, it is ensured that reflex activations
of given muscles occur at the appropriate times in the step cycle and are suppressed at other times [42],
as illustrated in Figure 4. This reflex activity, which regulates the timing and amplitude of the stepping
patterns [18,43,44], takes place at very specific moments in the gait cycle. In other phases of locomotion,
the motor cortex seems to become especially active. In particular, during normal walking, the tibialis
anterior (TA) shows two activity periods, one at the end of the stance and one at the end of the swing. It
has been suggested that the first burst is primarily due to output of a spinal CPG, whereas the second is
more of cortical origin [45]. Indeed, clinical observations on stroke patients clearly show that, especially,
the second burst (end swing) is affected after damage to the motor cortex. Additionally, transcranial
magnetic stimulation studies during gait have also pointed toward a strong involvement of the motor
cortex in the generation of this activity [46].
In other contexts than normal locomotion, sensory input from the skin also allows stepping to
adjust to unexpected obstacles [18]. The reflex mechanism, by its own, can give rise to a bipedal
locomotion system that is stable, reproduces human walking dynamics and leg kinematics, tolerates
ground disturbances and adapts to slopes without parameter interventions, as modeled in [47]. However,
arguments exposed hereafter indicate that human locomotion comprises more than a CPG network
modulated by reflexes.

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Figure 4. Model of the different pathways indicating how afferents can act on the central
pattern generator (CPG) during the stance phase of locomotion. The CPG contains a
mutually inhibiting extensor and flexor half-center (EHC and FHC, respectively). During the
stance phase, the load of the lower limb is detected by group I extensor muscle afferents and
group II (low threshold) cutaneous afferents, which activate the EHC. In this way, extensor
activity is reinforced during the loading period of the stance phase. At the end of the stance
phase, group Ia afferents of flexor muscles excite the FHC (which inhibits the EHC) and,
thereby, initiate the onset of the swing phase (this figure is reproduced with permission
from [42]; Copyright Elsevier, 1998).
2.4. Evidence of a Supra-Spinal Control
Although the existence of a CPG system modulated by sensory information has become broadly
accepted, many findings indicate that the cortex also plays a role of primary importance in human
walking [22]. Indeed, when lesions occur in the supra-spinal region of the central nervous system,
recovery of walking is extremely difficult and generally incomplete. This means that intact supra-spinal
centers are necessary for functional walking in humans.
Experiments with mammals also provide strong arguments in favor of a supra-spinal control.
For instance, after transection of their spinal cord, most cats are not able to generate locomotor
movements. This observation suggests that commands for the initiation of locomotor activity must
be given at a certain level above the lesion. By varying the level of transection, it was shown that the
regions for initiation of locomotion are located in the brain stem, i.e., at the supra-spinal level [48].
Furthermore, in paralyzed decerebrated cats, the initiation of ‘fictive’ locomotion (i.e., in the absence of
movement-related afferent feedback) can be realized with electrical stimulation of the mesencephalic
locomotor (MLR) region [49]. Such MLR regions have also been described in different vertebrate
species, including primates [50].
Another kind of evidence for supra-spinal control of locomotion is provided by the effects of
substances mimicking the action of descending pathways. Many studies have shown that a walking
pattern can be elicited in acute spinalized cats put on a treadmill after intravenous injection of such

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substances [51–53]. Furthermore, it is even possible to modify the walking pattern and modulate the
step cycle duration and step length by varying the nature of the injected drugs [54,55].
Furthermore, studies of direct stimulation of neurons in the motor cortex through transcranial
magnetic stimulation have shown that the motor cortex likely plays a role in activating the dorsiflexors
and plantarflexors during walking in humans [56]. Additionally, significant changes in motor and
cognitive demands (i.e., spatial attention) have been observed in the context of bipedal walking in
unknown or cluttered dynamic environments [57]. Functional neuroimaging studies have shown that the
primary motor cortex is recruited during rhythmic foot or leg movements [58]. Moreover, the technique
of functional near-infrared spectroscopy (fNIRS) has allowed for the detection of the involvement of
the frontal, premotor and supplementary motor areas during walking [59]. Electrophysiological studies
have also provided valuable information concerning the possible cortical origin of the intramuscular and
intermuscular electromyographic (EMG) synchronization (coherence) observed in lower limbs during
walking [60].
Finally, numerous studies have revealed strong arguments supporting the idea that motor centers in
the brain play an important and greater role in human walking compared with quadrupeds, as reviewed
in [6,61,62].
2.5. An Overview of the Human Locomotion Machinery
Figure 5gives a global picture of the human locomotion control process and summarizes the different
points discussed so far. Initiation of the movements, rhythm modulation and stopping come from the
superior central nervous system (i.e., the brain). Brain signals are sent to the spinal cord, where the
complex spinal circuitry manages to decode them (along with the feedback afferent signals coming from
the peripheral nervous system). The CPG network produces rhythmic patterns of neural signals. The
resulting command signals, called efferent impulses, are emitted by the αmotoneurons and transmitted
through the motor nerves to the muscles. By contracting in response to the nervous solicitations, muscles
produce active forces, which are transmitted to the skeleton through the tendons. The forces generate the
movements of the limbs. The feet interact with the ground, and external forces push the body forward.
The balance of the body is ensured by feedback thanks to the proprioceptive organs that respond to
mechanical stimuli by generating electrical impulses (action potentials). These action potentials are sent
back to the spinal cord through the afferent sensory nerves. The muscle spindles determine the muscle
fiber lengths and velocities, while the Golgi tendon organs provide information about the muscle forces.
Specific cutaneous mechanoreceptors located in the skin are able to detect tension, changes in texture,
rapid vibrations, sustained touch, pressure and stretches. Additional mechanoreceptors are also found in
the joints.
All this feedback information is integrated in the spinal cord in order to automatically stabilize the
walking, by means of reflexes (i.e., without intervention of the brain). This mechanism is valid for
limited perturbations and, in the case of important perturbations, the superior central nervous system and
the vestibulo-oculomotor system have to intervene, so as to prevent the fall.

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Figure 5. Global view of the human locomotion machinery (this figure is reproduced with
permission from [63]; Copyright Springer-Verlag, 2005; see text for details).
3. Supra-Spinal Control of Human Locomotion
In this section, we focus on the role of the brain in human locomotion control. Spatial organization
and temporal characteristics of supra-spinal control are described, with an emphasis on the information
that can be detected in a non-invasive way. These aspects are particularly important for the discussion
conducted in Section 4, which concerns the development of non-invasive brain-computer interface (BCI)
dedicated to walk rehabilitation systems.
3.1. Measuring Brain Activity
Brain imagery techniques are essential tools to investigate the spatial and temporal organization of
the supra-spinal centers involved, for instance, in human locomotion control. To date, these techniques
allow one to monitor two types of brain activities: first, the electrophysiological activity and, second,
the hemodynamic response of the brain.
The electrophysiological activity of the brain is produced both by the electro-chemical transmitters
exchanging information between the neurons and by the ionic currents generated within the neurons
themselves. Electrophysiological activity can be measured thanks to electroencephalography (EEG),
electrocorticography (ECoG), magnetoencephalography (MEG) and invasive electrical measurements
operated at the single neuron level.
The hemodynamic response of the brain allows one to distinguish active from less activated neurons.
Indeed, the blood releases glucose to active neurons at a greater rate than in the area of inactive neurons.
The presence of glucose and oxygen results in a surplus of oxyhemoglobin in the veins of the active
area. Hence, the local ratio of oxyhemoglobin to deoxyhemoglobin changes [64]. The variations

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of this ratio can be quantified by methods, such as functional magnetic resonance and near-infrared
spectroscopy, from which it is possible to build 3D maps of the brain activity. These kinds of methods
are often considered as indirect, because they measure the hemodynamic response, which, in contrast to
electrophysiological activity, does not directly characterize the neuronal activity.
In the following paragraphs, each brain imaging technique is explained. First, electrophysiological
methods are discussed, and then, metabolic methods are described. Table 2summarizes the different
imaging techniques by listing in each case the type of brain activity measured, the temporal and spatial
resolutions, safety and portability (adapted from [65]).
Table 2. An overview of neuroimaging methods. Direct methods detect electrical
or magnetic activity of the brain, while metabolic methods are considered as
indirect methods of imaging (adapted from [65]). EEG, electroencephalographic;
MEG, magnetoencephalography; ECoG, electrocorticography; LFP, local field potential;
MUA, multi-unit activity; SUA, single-unit activity; fMRI, functional magnetic
resonance imaging; SPECT, single-photon emission-computed tomography; PET,
positron-emission-tomography; NIRS, near-infrared spectroscopy.
Neuroimaging
Method
Activity
Measured
Temporal
Resolution
Spatial
Resolution Risk Portability
EEG Electrical ∼0.001 s ∼10 mm Non-invasive Portable
MEG Magnetic ∼0.05 s ∼5 mm Non-invasive Non-portable
ECoG Electrical ∼0.003 s ∼1 mm Slightly invasive Portable
Intracortical
neuron
recording
Electrical ∼0.003 s
∼0.5 mm (LFP)
∼0.1 mm (MUA)
∼0.05 mm (SUA)
Strongly invasive Portable
fMRI Metabolic ∼1 s ∼1 mm Non-invasive Non-portable
SPECT Metabolic ∼10 s–30 min ∼1 cm Non-invasive Non-portable
PET Metabolic ∼0.2 s ∼1 mm Non-invasive Non-portable
NIRS Metabolic ∼1 s ∼2 cm Non-invasive Portable
3.1.1. Electroencephalography
Electroencephalography (EEG) measures the electric brain activity caused by the currents induced by
neurons and during synaptic excitations of the dendrites [66]. The measurements are realized thanks to
electrodes placed on the scalp, thus in a non-invasive way. This explains why EEG is by far the most
widespread brain activity recording modality. With NIRS (see below), EEG is the only non-invasive
acquisition technique that is really portable. Moreover, it is relatively cheap and offers a high temporal
resolution (about 1 ms). However, EEG scalp electrodes are only able to measure the electrical potentials
of thousands of neurons, which are weakened and smeared by the volume conduction effect of the
skull [67], leading to signals of a few microvolts only and a poor global spatial resolution.
The weak amplitude of EEG signals renders them sensitive to electronic noise and artifacts. EEG
artifacts are spurious signals present in recordings and whose origin is not cerebral. They may arise
from the patient itself: the eyes, the tongue, the pharyngeal muscle, the scalp muscles, the heart or the
sweat glands all produce electrical potentials, which can influence the EEG measurement, especially

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if they are in movement. Skin resistance changes, due to sweating, may also badly affect the signals.
Electrical interference with a power line or surrounding electrical apparatus is another source of artifacts
that may be induced electrostatically or electromagnetically. Finally, artifacts may also arise from faulty
electrodes or the recording equipment itself.
In many cases, artifacts can be immediately identified by visual spatial analysis: high amplitude
potentials appearing at only one electrode are not likely due to cerebral activity. Indeed, brain produces
potentials that exhibit a physiological distribution characterized by a maximum voltage amplitude
gradually decreasing with increasing distance over the scalp. Likewise, rhythmical or repetitive
irregular signals appearing simultaneously in non-adjacent brain areas strongly suggest the presence
of artifacts [68].
Algorithms designed to detect and correct EEG artifacts integrate these principles and exploit
techniques, like temporal filtering, spatial filtering, independent component analysis (ICA) [69,70], blind
source search (BSS) [71] or thresholding of meaningful parameters (e.g., channel variance) based on a
prior statistical analysis [72].
EEG analysis of human locomotion is particularly complicated by experimental difficulties [73,74]:
in addition to “traditional” EEG artifacts (ocular, muscular, power line, etc.), EEG recordings realized
in ambulatory conditions are further degraded by additional sources of noise. Triboelectric noise is
generated by movement, friction and flexion of the cable components, resulting in a static or piezoelectric
movement transducer effect [75]. Electrode movements are produced by movements of the head, but
also by the shocks undergone by the whole body at each step, which, albeit significantly attenuated, are
transmitted to the head [76]. These movements modify the magnetic and capacitive coupling of the user
and the electrode leads, leading to an alteration of the parasitic current flowing into the leads [77]. A
resulting parasitic voltage drop is then produced in the electrode/gel/skin interface, which interferes with
the EEG signal [78]. Finally, electrode movements can also cause impedance variation, which directly
affects the electrode voltage offset [79].
Unfortunately, all these motion artifacts are not limited to a small spectral band, so they cannot be
simply removed by frequency filtering. In a study conducted to assess EEG signal quality in motion
environments [80], it is shown that EEG spectra in the walking (or jogging) condition exhibit frequency
peaks consistent with the fundamental stride frequency, as well as its harmonics. The authors also
state that motion artifacts affect signal integrity most prominently at low frequencies (i.e.,<4 Hz)
during steady walk. Nevertheless, the study also shows that traditional N1 and P300 event-related
potentials (ERP) elicited during a standard auditory discrimination task (i.e., “oddball paradigm”) are
not dramatically affected by the walking condition, either in amplitude, in topographic distribution or
response time (70% of acceptable trials across all participants). This is, however, not the case for the
jogging condition, for which only 14% of trials were accepted.
Analog conclusions are drawn in more recent studies, where subjects are standing or walking on a
treadmill while performing a visual oddball response task [74,81].
3.1.2. Magnetoencephalography
Magnetoencephalography (MEG) detects the weak magnetic fields resulting from the intracellular
electrical currents in neurons. The neurophysiological processes that produce MEG signals are the same

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as those that produce EEG signals. The advantage of MEG is that magnetic fields are less distorted by
the skull and scalp than electric fields. This technique offers a spatial resolution of a few millimeters
and a temporal resolution of a few milliseconds [82], but requires highly sensitive devices [arrays of
SQUIDs (superconducting quantum interference devices)] cooled to a few degrees Kelvin. Additionally,
measurements must be realized in a shielded room in order to minimize interferences with magnetic
fields from external sources. This non-invasive technique gives only access to shallow parts of the brain
and is too bulky and expensive to become an acquisition system suitable for everyday use.
3.1.3. Electrocorticography
Electrocorticography (ECoG) consists of implanting electrodes under the dura mater, directly on the
surface of the cortex, without penetrating it. This technique represents a partially invasive compromise,
offering a good signal quality and spatial resolution [83]. Compared to EEG, ECoG provides higher
temporal and spatial resolution. ECoG signals are characterized by higher amplitudes and a lower
vulnerability to artifacts, such as blinks and eye movement [84]. However, this technique is invasive and
requires a surgical intervention in order to implant an electrode grid. This operation is thus risky. Early
experiments with animals indicated that stable ECoG signals could be recorded over several months [85].
More recent studies with monkeys also indicated that ECoG electrodes remained stable during several
months [86]. Nevertheless, the long-term stability of EGoG signals remains unclear to date.
3.1.4. Intracortical Neuron Recording: Brain Implants
Brain implants may be directly inserted into the grey matter of the brain, in order to measure the
electrical activity of single neurons. Three types of signals can be obtained with this technology:
single-unit activity (SUA), multi-unit activity (MUA) and local field potentials (LFPs) [87]. The SUA
is obtained by high-pass filtering (>300 Hz) of the signal of a single neuron. MUA contains the
contribution of multiple SUAs. LFPs are computed by low-pass filtering (<300 Hz) of the neuron
activity. LFPs are analog signals, whereas SUA and MUA contain the spiking activity of single neurons.
Brain implants provide the best quality of signals, with a much higher spatial and temporal resolution
than EEG recording. In 2005, such a type of neurosurgery was done successfully with a tetraplegic, who
was subsequently able to move, only by thought, a cursor on a computer screen, as well as an artificial
hand [88]. Nevertheless, this technique requires a heavy and risky surgical operation. Additionally, such
devices raise several issues, like long-term viability and biocompatibility [89].
3.1.5. Functional Magnetic Resonance Imaging
Functional magnetic resonance imaging (fMRI) is a non-invasive technique allowing one to determine
the blood oxygen level variations that occur during brain activity (higher neural activity requires more
glucose and oxygen). The main advantage of this technique is a high spatial resolution, of the order
of the millimeter, which makes it perfectly suitable for accurately localizing active regions inside the
brain [90]. fMRI suffers from a poor time resolution of about one or 2 s. On top of this, this technique
is highly susceptible to head motion artifacts. Like for MEG, fMRI requires cumbersome and very
expensive equipment, which is not really suited for individual and everyday applications.

Brain Sci. 2014,415
3.1.6. Nuclear Functional Imaging Techniques
Single-photon emission-computed tomography (SPECT) is an imaging technique based on the
tracking of gamma rays emitted by radionuclides injected in the bloodstream of the patient. Specific
chemicals (radioligands), by their particular binding properties to certain types of tissues (e.g., brain
tissues), allow one to concentrate the radionuclides in the region of interest of the body, thus making
them visible to the gamma cameras of the system [91]. SPECT is a tomography tool that provides 3D
information and can reconstruct an image of a thin slice along any chosen axis of the body. The spatial
resolution of SPECT is about 1 cm, and several dozens of seconds are needed for a single projection (a
full 360◦scan by 5◦steps takes up to 30 min). When used for functional brain imaging, the system is
able to assess the cerebral blood flow, which is directly linked to the local brain metabolism.
Positron-emission tomography (PET) is relatively similar to SPECT. The radionuclides injected in the
patient emit positrons, which annihilate with electrons located in the vicinity (a few mm) and, therefore,
produce a pair of gamma rays emitted in opposite directions [92]. By detecting the two gammas in
coincidence mode, enhanced spatial information is available for the imaging algorithms. Consequently,
a better spatial resolution is reached, compared to the SPECT technique. In brain imaging applications,
the active molecule generally chosen is FDG, an equivalent of glucose. Again, brain metabolism is
assessed in this particular application of the technology.
3.1.7. Near-Infrared Spectroscopy
Near-infrared spectroscopy (NIRS) is another non-invasive acquisition technique. It determines the
variations of hemoglobin concentrations linked to neural activity by detecting changes in the optical
response (absorption, scattering) of cerebral tissue to near-infrared light. Infrared light penetrates the
skull to a depth of approximately 1–3 cm below the surface. Thus, only the outer cortical layer can be
imaged using this technique. A further limitation of the technique lies in the fact that hemodynamic
response occurs a certain number of seconds after its associated neural activity [93]. The spatial
resolution of this technique is of the order of the centimeter, while the time resolution is of approximately
200 ms. Contrary to MEG and fMRI, NIRS is an appropriate measurement modality for everyday use,
as its equipment is relatively cheap, portable [94], simple to attach and requires little user training [95].
3.2. Spatial Organization of Supra-Spinal Control
Neuroimaging of gait is not straightforward, and practical problems are posed, since the majority of
imaging techniques (like PET, fMRI and EEG) require that subjects do not move their head during the
experiments. Moreover, functional brain imagery of subjects walking on a treadmill does not allow one to
discriminate whether the evoked activity is due to sensory input or motor input. Consequently, alternative
neuroimaging techniques have to be employed, like, for instance, recording cerebral activity during
motor planning of walking prior to walking initiation, using tasks that share some cerebral processes with
gait, without the need to engage in actual gait (like motor imagery of gait or repetitive foot movements).
The results obtained using these different strategies are detailed below and summarized in Table 3.

Brain Sci. 2014,416
Table 3. An overview of the results obtained by different functional neuroimaging studies
of gait in healthy subjects.
Publication Neuroimaging
Method
Experimental
Approach Key Findings
Fukuyama et al., 1997 [96]
SPECT Real gait (on ground) During gait, increased activity in the supplementary motor
area (SMA), medial primary sensorimotor area, striatum,
cerebellar vermis and visual cortex
Hanakawa et al., 1999 [97] SPECT Real gait (on treadmill) Cerebral activity during walking also observed in the
dorsal brainstem
Miyai et al., 2001 [98] NIRS Real gait (on treadmill) Walking increases cerebral activity bilaterally in the medial
primary sensorimotor cortices and the SMA
Suzuki et al., 2004 [99] NIRS Real gait at different
speeds (on treadmill)
Increase of cerebral activity in the prefrontal cortex and
premotor cortex as locomotor speed increases; cerebral
activity in the medial sensorimotor cortex not influenced by
locomotor speed
Malouin et al., 2003 [100] PET
Motor imagery of standing,
gait initiation, real walking,
walking with obstacles
Motor imagery of walking increases activity in the pre-SMA
(compared to imagined standing); in the left visual cortex and
caudate nucleus (compared to imagery of gait initiation)
Jahn et al., 2004 [101] fMRI Motor imagery of standing,
walking and running
Cerebellar activation increased during motor imagery of
running, not during motor imagery of walking and standing;
vestibular and somatosensory cortex were deactivated during
running, but not during walking
Miyai et al., 2001 [98] NIRS/fMRI Repetitive foot movements Foot-extension flexion movements generate a similar brain
activation pattern to that associated with walking
Sahyoun et al., 2004 [102] fMRI Active vs. passive
foot movements
During active movements, an increase of cerebral activity
in the somatosensory cortex, SMA, cingulate motor area,
secondary somatosensory cortex, insular cortices, putamen,
thalamus and cerebellum
De Jong et al., 2002 [103] PET Antiphase flexion and
extension movements
Cerebral activations distributed over the right anterior parietal
and right dorsal premotor cortex
Christensen et al., 2000 [104] PET Bicycle movements
Both passive and active bicycling increase cerebral activity
bilaterally in primary sensorimotor cortices, SMA and the
anterior part of the cerebellum.
La Foug`
ere et al., 2010 [105] PET/fMRI Real vs. imagined locomotion
During real and imagined locomotion: activations in the
frontal cortex, cerebellum, pontomesencephalic tegmentum,
parahippocampal, fusiform and occipital gyri; deactivations
in the multisensory vestibular cortices (superior temporal
gyrus, inferior parietal lobule). Real steady-state locomotion
seems to use a direct pathway via the primary motor cortex,
whereas imagined modulatory locomotion uses an indirect
pathway via a supplementary motor cortex and basal
ganglia loop.
3.2.1. Execution of Real Gait
Despite the experimental difficulties mentioned above, a few techniques allow one to assess cerebral
activity during actual gait.
Both SPECT and PET scans, for instance, may be used to study brain activity during actual gait.
Indeed, these techniques allow one to separate, in time, task performance from image acquisition.
When radioactive substances are injected intravenously during gait, they are rapidly distributed in the
brain proportionally to local cerebral blood flow and, most importantly, remain in the brain for hours.
Therefore, the spatial distribution of radionuclides at the time of PET or SPECT scanning reflects
the pattern of cerebral perfusion at the time of injection. Using this approach, a significant increase
in cerebral activity was found during gait in the supplementary motor area (SMA), medial primary
sensorimotor area, striatum, cerebellar vermis and visual cortex [96]. This was the first study to show
changes in cortical activity during walking in human subjects, compared to the resting state. Later on,
the same group demonstrated that a significant cerebral activity during walking is also observed in the

Brain Sci. 2014,417
dorsal brainstem [97]. This finding is important, because it is one of the few observations suggesting the
presence of brainstem locomotor centers in humans.
Cerebral activity can also be monitored while subjects are walking on a treadmill thanks to NIRS.
This technique allows the comparison of several experimental conditions. In [106], the cerebral activities
evoked during gait, alternating foot movements, arm swing and motor imagery of gait were compared.
The results of this study indicated that the gait-related responses along the central sulcus were medial and
caudal to the activity associated with arm swing. This is quite in agreement with the known somatotopic
organization of the motor cortex. Crucially, these authors showed that walking increased cerebral activity
bilaterally in the medial primary sensorimotor cortices and the SMA, and to a greater extent than the
alternation of foot movements.
In another NIRS study, the effect of different walking speeds on cerebral activity was examined.
It was demonstrated that cerebral activity in the prefrontal cortex and premotor cortex tend to increase as
locomotor speed increases, whereas cerebral activity in the medial sensorimotor cortex is not influenced
by locomotor speed [99].
3.2.2. Gait Initiation
As already mentioned in this review, EEG recording during walking is particularly challenging, due
to motion artifacts. However, some researchers have published EEG studies prior to and/or during
gait initiation [107,108]. This experimental approach offers two advantages. First, it provides a high
temporal resolution analysis of the electrical brain activity in an action where changes in sensory input
are minimal. Second, motion artifacts are drastically reduced, because the recording is realized before
the onset of any movement. In these studies, stronger event-related potentials were found in the medial
central region (Cz) when comparing EEG activity preceding externally-cued gait initiation with activity
preceding foot dorsiflexion. This EEG difference indicates that the medial frontal cortex, above its role
in initiating a simple foot movement, supports the initiation of gait [109].
3.2.3. Motor Imagery of Gait
Another strategy to assess the cerebral bases of true gait control consists of investigating motor
imagery of gait, i.e., the mental simulation of gait without actual execution. This approach presents
the advantage of being totally compatible with techniques like fMRI and PET, which provide relatively
high spatial resolution and whole-brain coverage. Numerous studies have been published on the subject.
Cerebral activity evoked during motor imagery of standing, initiating gait, walking and walking with
obstacles was analyzed in [100]. The authors report that motor imagery of walking increased cerebral
activity in the pre-SMA when compared to imagined standing, and in the left visual cortex and caudate
nucleus when compared to imagery of gait initiation. Comparing motor imagery of walking with or
without obstacles increased cerebral activity in the precuneus bilaterally, the left SMA, the right parietal
inferior cortex and the left parahippocampal gyrus. This illustrates that the neuronal circuitry of gait can
extend beyond motor cortex, and it can be modulated by the difficulty of the imagined locomotor task.
In another study, based on fMRI, motor imagery of standing, walking and running was studied [101].
The results obtained indicate an increase in the activation of the cerebellum during motor imagery

Brain Sci. 2014,418
of running, but not during motor imagery of walking and standing. Additionally, vestibular and
somatosensory cortex were deactivated during running, but not during walking. As summarized in [109],
these findings suggest that the speed of gait is under the control of a cerebellar locomotor center and that
cortical processing of vestibular and somatosensory information is particularly important during walking.
3.2.4. Repetitive Leg or Foot Movements
A last approach to assess the supra-spinal control of human locomotion is to study repetitive leg or foot
movements. Indeed, it is thought that these movements rely partly on the same neural processes as those
used during actual gait. In a combined NIRS and fMRI study, it was shown that foot extension-flexion
movements indeed generate a similar brain activation pattern to that associated with walking [98].
Like motor imagery of gait, the study of leg or foot movements presents practical advantages, like
the reduction of motion artifacts and the possibility of using cumbersome brain imagery techniques.
Of course, one does not study real gait in this case, since this motor task additionally requires the
coordination of a large number of body parts and includes the integration of balance control information.
Using fMRI to compare active vs. passive unilateral foot extension-flexion movements, it was found
that during active compared to passive foot movements, cerebral activity increased in the somatosensory
cortex, SMA, cingulate motor area, secondary somatosensory cortex, insular cortices, putamen, thalamus
and cerebellum [102]. This suggests that both cortical and subcortical structures are involved in the motor
control of rhythmic foot movements.
In a PET study, cerebral activity during antiphase flexion and extension movements of the two upper
and the two lower limbs was examined [103]. For both the arms and legs, cerebral activations related to
antiphase movements were distributed over the right anterior parietal and right dorsal premotor cortex,
suggesting that these structures support the sensorimotor integration required for antiphase movements.
Inter-limb coordination study was assessed with bicycle movements in [104]. The results obtained
in this analysis showed that both passive and active bicycling increase cerebral activity bilaterally in
primary sensorimotor cortices, SMA and the anterior part of the cerebellum. After subtraction of passive
from active bicycling, significant activation was found in the leg area of the primary motor cortex and
the precuneus. This suggests that significant cerebral control is involved in the production of rhythmic
movements, such as bicycling.
3.2.5. Summary
Thanks to the numerous neuroimagery studies of gait (or assimilated motor tasks), the supra-spinal
control of human locomotion has been identified as lying in different centers in the brainstem, cerebellum
and cortex (cf. Figure 6). This cerebral network is believed to modulate locomotion (e.g., gait initiation,
termination, velocity, direction and spatial orientation) and to control balance and gait by integration of
multi-sensory information [110]. The most important regions are the cerebellar locomotor region (CLR),
the mesencephalic locomotor region (MLR) and the subthalamic locomotor region (SLR).

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Figure 6. Comparison of real (PET) and imagined locomotion (fMRI) brain activations
(this figure is reproduced with permission from [105]; Copyright Elsevier, 2010). Sagittal
midline and render views are shown. It can be seen that during real locomotion, the
primary motor sensory cortices (pre- and post-central gyri) are active (left) as compared
to the supplementary motor areas (superior and medial frontal gyri) in mental imagery of
locomotion (right). Furthermore during imagined locomotion, the basal ganglia (caudate
nucleus, putamen) are active, which is not the case for real locomotion.
Remarkable similarities exist between the real and imagined locomotion networks [105]. The first one
is the activation of the midline cerebellar area, which controls body and trunk balance, and the cerebellar
locomotor region, which is thought to regulate speed and gives rhythmical impulses to the brainstem
and spinal cord [101]. The second remarkable similarity in both paradigms is the activation of occipital
visual cortices, which are related to visual processing.
Significant differences between real and imagined locomotion networks have also been found [105].
Whereas the primary motor cortex is activated (in the functional region of the leg) during real locomotion,
supplementary motor areas (superior and medial frontal cortex, dorsolateral prefrontal cortex) and
basal ganglia (caudate nucleus, putamen) are activated during mental imagery. The most acceptable
explanation suggested for this is that the premotor and basal ganglia activations in imagined locomotion
could reflect an indirect pathway of locomotion that is responsible for the modulation of locomotion,
whereas the primary motor activations in real continuous walking utilize a direct pathway for a
steady-state of locomotion (cf. Figure 7).

Brain Sci. 2014,420
Figure 7. Illustration of the executive and planning networks of locomotion, as suggested
in [105]. Execution of locomotion in a non-modulatory steady state (left side) goes from the
primary motor cortex areas directly to the spinal central pattern generators (CPG), thereby
bypassing the basal ganglia and the brainstem locomotor centers. A feedback loop runs
from the spinal cord to the cerebellum and, thereby, via the thalamus to the cortex. For
planning and modulation of locomotion (right side), cortical locomotor signals originate
in the prefrontal supplementary motor areas and are transmitted through the basal ganglia
via disinhibition of the subthalamic locomotor region (SLR) and mesencephalic locomotor
region (MLR), where they converge with cerebellar signals from the cerebellar locomotor
region (CLR). The MLR functionally represents a cross point for motor information form
basal ganglia and cerebellar loops. Descending anatomical projections are directed to
the medullary and pontine reticular formations (PMRF) and the spinal cord; ascending
projections are in the main part concentrated on the basal ganglia and the non-specific nuclei
of the thalamus (not shown for the sake of clarity). The CLR also projects via the thalamus
back to the cortex. Cortical signals are furthermore modulated via a thalamo-cortical-basal
ganglia circuit. The schematic drawing shows a hypothetical concept of a direct pathway
of steady-state locomotion (left) and an indirect pathway of modulatory locomotion (right).
SMA, supplementary motor cortex. This figure is reproduced with permission from [105];
Copyright Elsevier, 2010.
3.3. Temporal Characteristics of Supra-Spinal Control
Functional brain imaging techniques have brought a lot of useful information to localize the cerebral
centers involved in human locomotion control. The weak point of these techniques, however, reside
in the poor temporal resolution they offer. In that regard, electroencephalography (EEG) represents an
interesting complementary technique for investigating neural processes governing walk and, particularly,
the dynamics of the brain.

Brain Sci. 2014,421
Detailed biophysical studies have revealed that single neurons are characterized by a complex
dynamics, with the ability to resonate and oscillate at multiple frequencies. Precise timing of their
activity within neuronal networks encode information, and synchronous activity of oscillating networks
is thought to link the single neuron activity to global behavior [111]. The control of precise actions, like
locomotion, for instance, requires the integration of multiple pieces of information and, thus, synchrony
of different convergent inputs. One of the roles of oscillatory activities in the brain is to operate this
synchrony. Indeed, oscillation-based synchrony is the most energy-efficient physical mechanism for
temporal coordination [111]. In that regard, it is fundamental to analyze brain dynamics to understand
the mechanisms involved in the supra-spinal centers during locomotion.
However, electrophysiological investigation of the cerebral activity elicited during walking is highly
challenging. Indeed, head and body movements constitute an important source of mechanical artifacts
strongly affecting the EEG signal quality.
Consequently, the main strategy generally used to overcome these experimental difficulties consists
of focusing on simplified foot or leg movements, which imply common cerebral processes with
gait. In these experimental protocols, subjects are mainly static and produce only limited lower limb
movements. A strong advantage of this approach is, of course, that motion artifacts are drastically
limited. In this case, however, the full neural activity related to walk is not available and, for instance,
cerebral processes involved in posture and balance control are missing. Recording EEG signals of
subjects walking on a treadmill include, of course, all these aspects, but then requires a powerful
analysis technique to discriminate the different artifact contributions from the real cortical signal.
Analysis results of these two approaches, static, on the one hand, and dynamic, on the other hand,
are reviewed hereinafter.
3.3.1. Electrocortical Potentials Related to Lower Limb Activation in a Static Condition
The cortical activity associated with bilateral anti-phase and in-phase rhythmic foot movements
produced by subjects sitting in a chair was investigated in [112]. In this study, the authors found
significant corticomuscular coherence between EEG signals and the anterior tibial muscles, at the
stepping frequencies in the central midline region, extending further to the frontal mesial area. During
isometric co-contraction of the calf muscles, coherence appeared between 15 and 30 Hz, concentrated
on the central midline area [Cz-central-parietal (CPz) electrodes]. This is the first study demonstrating
that there exists a representation of rhythmic foot motor patterns in the cortex, transmitted to the muscles
and fed back to the cortex with delays compatible with fast corticospinal transmission, which may be
important for gait control.
Assisted lower limb movements have also been investigated using electroencephalography [113].
In this study, subjects performed standardized, assisted stepping movements (i.e., mimicking walk)
in an upright position, while being secured to a tilt table. Electrocortical sources associated with
the movement-related potential were localized in the primary motor cortex, the premotor cortex,
the supplementary motor cortex, the cingulate cortex, the primary somatosensory cortex and the
somatosensory association cortex (i.e., in accordance with the findings of functional brain imagery).
The authors demonstrated that a clear succession of activations and deactivations was present in the
movement-related potential, in direct relationship with specific phases of the gait-like leg movements.

Brain Sci. 2014,422
In particular, it was shown that cortical activity was the greatest during transition between flexion and
extension of the legs and vice versa.
In [114], a non-invasive EEG-based BCI governing a functional electrical stimulation (FES)
system for ankle movement is presented. In this application, healthy subjects perform repetitive foot
dorsiflexions. EEG patterns underlying this action are detected in real time, and this information is
subsequently used to trigger the FES of the tibialis anterior of the contralateral foot, so as to achieve
its dorsiflexion. In fact, the trigger (or non-trigger) information is given by a linear Bayesian classifier
trained using a vector of spatio-spectral features, which optimally discriminate the idling and dorsiflexion
states. The authors state that analysis of subject-specific prediction models demonstrated that the EEG
power changes in the µ,βand low γbands observed over mid-central areas (i.e., electrode Cz) were the
most informative features for classification. This likely corresponds to activity within the primary motor
cortex foot representation area and/or supplementary motor area (which is not surprising from a brain
anatomy standpoint) and is in perfect agreement with prior studies [115,116].
3.3.2. Electrocortical Potentials Related to Walking
The first analysis of EEG during walking on a treadmill was published by Gwin and co-workers [117].
By using a method based on independent component analysis (ICA) combined with an inverse modeling
approach, the authors claimed they could discriminate electrocortical sources, muscle sources and other
artifacts from the raw EEG signals. They found that cortical activity in the anterior cingulate, posterior
parietal and sensorimotor cortex exhibited significant and smooth intra-stride changes in spectral power.
More precisely, alpha and beta band spectral powers increased in or near the left/right sensorimotor and
dorsal anterior cingulate cortex at the end of each stance phase (i.e., as the leading foot was contacting the
ground and the trailing foot was pushing off). According to this study, power increases in the left/right
sensorimotor cortex were more important for contralateral limb push-off (ipsilateral heel strike) than for
ipsilateral limb push-off (contralateral heel strike). Finally, the authors reported evidence of intrastride
high-gamma spectral power changes in anterior cingulate, posterior parietal and sensorimotor cortex.
In parallel, Presacco and co-workers [118] showed for the first time that the kinematics of the ankle,
knee and hip joints during human treadmill walking can be inferred from EEG signals. Successful
decoding of these signals was done basically by filtering them (0.1–2 Hz) and passing them through
a linear autoregressive model. According to this study, gait trajectories were inferred with accuracies
comparable to those from neural decoders based on multiple single-unit activity recorded in non-human
primates [1]. The results of this study indicate a high involvement of a fronto-posterior cortical network
in the control of walking and suggest that EEG signals can be used to study, in real time, the cortical
dynamics of walking and to develop brain-machine interfaces aimed at restoring human gait function.
3.3.3. Spatio-Frequential Characteristics of the Detected Potentials
From the spatial point of view, all the studies found activations of the brain globally compatible
with the primary motor cortex’s foot representation area and/or supplementary motor area, except one
(cf. Table 4). Indeed, Presacco and co-workers [118] report the activation of a complex, distributed and
sparse cortical network, in which scalp areas over anterior, right lateral and right anterior-occipital scalp

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areas seem to equally contribute (at least to their decoding of the kinematics of the right leg, for subjects
walking on a treadmill). The same results were published in [119].
Table 4. EEG studies of human locomotion: a schematic view of recent results
obtained with static and dynamic experimental protocols. M1 is the primary motor
cortex; PMC is the premotor cortex; SMA is the supplementary motor cortex; CC is the
cingulate cortex; S1 is the primary somatosensory cortex; and SA is the somatosensory
association cortex; Cz is the medial central region. BCI, brain-computer interface;
FES, functional electrical stimulation; ICA, independent component analysis; ERD,
event-related synchronization; ERS, event-related desynchronization; CCA, canonical
correlation analysis; ERSP, event-related spectral perturbation.
Publication Aim of the Study Approach/
Cleaning Method Activated Brain Areas Frequency Bands of Interest
Raethjen et al., 2008 [112]Rhythmic foot
movements
Static/
no cleaning
Central midline region
and frontal mesial area
Stepping frequency and
βband (15–30 Hz)
Wieser et al., 2010 [113]Assisted lower-limb
movements
Static/
no cleaning
M1, PMC, SMA,
CC, S1, SA
No frequency analysis.
Activations are directly
related to specific phases of
the gait-like movements
Do et al., 2011 [114]
BCI dedicated to
a FES system for
ankle movement
Static/
no cleaning
Mid-central areas
(electrode Cz) µ,βand low-γbands
Gwin et al., 2011 [130]EEG activity during
treadmill walking
Dynamic/ICA
cleaning (AMICA)
Anterior cingulate,
posterior parietal and
sensorimotor cortex
αand βbands and clear
evidence of high-γintra-stride
spectral power changes
Presacco et al., 2011 [118]
Neural decoding of
treadmill walking
from EEG signals
Dynamic/
no cleaning
Involvement of a broad
fronto-posterior
cortical network
Delta band (0.1–2 Hz)
Severens et al., 2012 [125]Detection of ERD/ERS
during walking
Dynamic/
CCA cleaning
ERD found in the µband
above the electrode Cz
and in the βband
above the lateral motor
cortex (electrodes
C3 and C4).
ERSPs in µand βbands
are coupled to the gait cycle
with significant differences
between left swing, right
swing and double support
phase of the gait cycle
Wagner et al., 2012 [127]Robotic-assisted
treadmill walking
Dynamic/
ICA cleaning
(Infomax)
Central midline areas
µand βrhythms suppressed
during active walking in the
Lokomat; modulations of the
lower γband (25 to 40 Hz)
related to the phases of the gait
cycle; these might be related
to sensorimotor processing of
the lower limbs
Petersen et al., 2012 [129] Treadmill walking Dynamic/
coherence analysis
Significant coherence
between EEG (Cz) and
EMG (tibialis anterior)
before the heel strike
Coherence between 24
and 40 Hz; evidence that
the coupling is not due to
non-physiological artifacts
From the frequential point of view, spectral power variations were generally found from alpha to
gamma bands, but, astonishingly, a successful neural decoding of treadmill walking was realized by
Presacco and co-workers [118] using EEG signals band-pass filtered between 0.1 and 2 Hz. This is
particularly surprising, because it was shown in two other studies, conducted to assess EEG signal quality
in motion environments [74,80], that EEG spectra in the walking (or jogging) condition exhibit frequency
peaks consistent with the fundamental stride frequency, as well as its harmonics. The authors in [80]
also state that motion artifacts affect signal integrity most prominently at low frequencies (i.e., the delta
band) during steady walk. In their analysis protocol, Presacco and co-workers [118] do not mention

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any pre-processing method aiming at either correcting or discriminating these motion artifacts from the
real cortical signals. The only way for them to make the choice of this frequency band legitimate is
the fact that good results are obtained and, moreover, other studies exploited the same portion of the
EEG spectrum to decode upper limb movements. We strongly emphasize the fact that, in the latter
studies, no motion artifact due to gait is produced. Consequently, this might suggest that the decoding of
kinematics of walking (periodical movement) on the basis of the EEG signals is done by Presacco and
co-workers [118] with a linear autoregressive model exploiting the periodical motion artifacts present
in the EEG recordings. This option is furthermore supported by the fact that no spectral information is
given under 3 Hz in the study of Gwin [120].
This last point is corroborated by a recent paper from Antelis and co-workers [121]. These
authors have focused on several publications in which are reported successful reconstructions of
different limb kinematics from EEG using the low frequency activity of the EEG and linear regression
models [122–124]. Antelis and co-workers [121] showed that the mathematical properties of the linear
regression model and of the correlation metric used in these studies could explain the good results
reported. Moreover, they demonstrated that correlation results obtained with real EEG signals, shuffled
or random EEG data were not statistically different. This means that the linear models developed
in [122–124] are able to provide the same results irrespective of the presence or absence of limb velocity
information in EEG signals.
Other recent studies dedicated to EEG analysis during a locomotion task may be added to this
discussion. Severens and co-workers [125] investigated the possibility of measuring event-related
desynchronizations (ERDs) [126] and event-related spectral perturbations (ERSPs) during walking on a
treadmill. After cleaning EMG artifacts using canonical correlation analysis (CCA), they found an ERD
in the mu band above the central motor cortex (electrode Cz) and in the beta band above the lateral motor
cortex (electrodes C3 and C4). In addition, they found that ERSPs in mu and beta bands were coupled to
the gait cycle with significant differences between the left swing, right swing and double support phase
of the gait cycle. They did not report any signal of cortical origin at low frequency. Indeed, as the low
frequency modulations they found in the ERSPs were also visible in the occipital channels, the authors
explained that these were very unlikely related to brain activity and probably due to remaining artifacts.
Wagner and co-workers [127] also showed that mu and beta rhythms are suppressed during active
walking in the Lokomat, a robotic gait orthosis. They also provided evidence of modulations of the
lower gamma band (25 to 40 Hz), localized in central midline areas and related to the phases of the
gait cycle. For different reasons, the authors speculate that these activations and deactivations might be
related to sensorimotor processing of the lower limbs in the complex motor pattern of human locomotion.
Although their ERSPs plots exhibit ERD and ERS around and below 5 Hz, they neither comment on them
nor claim that these originate from cortical activity.
3.3.4. About the Origin of the Detected Signals
Among all the works described in previous paragraph, only [112] tried to determine the origin of
the information flux contained in the studied signals (descending commands from the brain or sensory
feedback sent to the brain). This is done by computing time delays between EEG time series and
electromyographic activity of the involved lower limb muscles by means of the “maximizing coherence

Brain Sci. 2014,425
method” [128]. Actually, the other studies presented in previous paragraph do not consider this
aspect and give no indication on the direction of the brain-muscle interaction (i.e., if it is up-going or
down-going). It is therefore unknown, for instance, if the intra-stride spectral power variations found by
Gwin[120] are due to voluntary movements or sensory feedback (or a combination of both). The same
question arises concerning the EEG decoding presented by Presacco and co-workers [118]. Resolving
this ambiguity is particularly crucial, though, for the development of gait rehabilitation systems. Indeed,
if the information detected in the EEG signals is purely due to the sensory feedback of the gait-related
movements, it would be unusable to drive any device, given that no valid prediction of a movement can
be done exploiting sensory information resulting from it.
Most importantly, studying EEG signals in treadmill walking also requires the need to exclude
gait-related artifacts. Too few studies tackle this issue [73]. In particular, [117] used an ICA analysis
coupled with an inverse solution approach. These authors claim that they could disentangle muscular
contributions and other artifacts from real cortical signals. However, in a previous study, the very same
authors [120], using the very same dataset, clearly stated that:
“Unlike more spatially stationary artifacts in EEG signals arising from eye movements, scalp muscles,
fMRI gradients, etc., which may be resolved by ICA decomposition into a subspace of one or more
independent components, we found that gait-related movement artifact remained in many if not most
of the independent components. This prevented us from removing only a small subset of components
capturing the movement artifacts.”
For this reason, they considered the removal of motion artifacts from the EEG during walking
and running on a treadmill using an artifact template subtraction method. Such a method allowed
for enhancing the detection of P300 potentials in ambulatory conditions. Nevertheless, the study of
cerebral processes involved in human locomotion is not possible using a subtraction method, as it would
undoubtedly remove interesting signals from the EEG recordings. For this reason, the authors used
only the ICA approach to clean the EEG signals [117]. In this study, the issue of motion artifacts was
completely eluded, and no mention was made of any appropriate treatment to reject them. Thus, it can
be doubted that the time-frequency analysis plots shown in that paper do not contain any motion artifact
contribution. In the discussion conducted in [73], it was shown that a time-frequency analysis of the
signal of an accelerometer placed on the head of a subject walking at 1.67 m/s on a treadmill presented
periodic power spectral changes over large frequency bands, in a similar way to the results obtained after
ICA by [117].
Finally, it should be noted that on the basis of a spectral analysis, it is not possible to determine
which cortical region is directly involved in the transmission of motor commands to the muscles. In
contrast, coherence analysis reveals anatomical coupling between cortical activity and the motor output
to the muscles by detecting common rhythmicities in EMG and EEG signals. In the study conducted
in [129], the coupling between electroencephalographic (EEG) and electromyographic (EMG) signals
from leg muscles during treadmill walking was investigated. The authors report significant coherence
between EEG signals recorded over the leg motor area (Cz electrode) and EMG from the tibialis anterior
(TA) muscle in the 24–40 Hz frequency band before the heel strike, during the swing phase of the gait
cycle. The presence of a significant imaginary part of the complex coherence indicates that the coupling
in the study was not due to non-physiological artifacts. The negative sign of this imaginary part of

Brain Sci. 2014,426
the coherence suggests that the cortical activity was leading the muscle activity. Time lag estimates
between EEG and EMG signals are consistent with the typical cortico-spinal conduction times. This
result indicates that rhythmic cortical activity in this particular frequency band is transmitted to the
lower limb muscles during walking, at specific moments in the gait cycle. This work thus proves and
confirms that the motor cortex directly contributes to the muscle activity involved in human locomotion.
On top of this, according to the significant coherence values (24–40 Hz around Cz) found by [129], the
multiple ERD-ERS detected by Gwin [130] in the 3–24 and 40–76 Hz bands are obviously not indicative
of a direct corticospinal drive, at least, not to the tibialis anterior. Thus, one may think that these signals,
if not affected by residual artifacts, would rather reflect the control of sensory afferents (i.e., one of the
hypotheses formulated by the authors in [130] themselves). It is interesting to note that the studies by
Wagner [127] and Severens [125] do not report multiple ERD-ERS in the α,βand γbands and are in
line with the coherence study made by [129].
3.4. Results from Invasive Studies
Although this paper is essentially devoted to non-invasive analyses of human locomotion, it may
be interesting to report a few important results from invasive studies in order to bring supplementary
information to the different elements presented above.
Several invasive studies with mammals report rhythmic cerebral activations in phase with the gait
cycle. As mentioned in [131], olivary neurons, in rats, discharge rhythmically at frequencies closely
matching the step cycle [132]. Analogously, in cats, the locomotion activity of more than 90% of
neurons of motor cortex are modulated in the rhythm of strides [133]. Moreover, it was shown that
the discharge rate means, peaks and depths of stride-related frequency modulation changed dramatically
during accurate stepping, as compared with simple walking [134].
In a recent breakthrough [1], bipedal walking patterns could be extracted from the modulations of
discharge rates of monkey primary somatosensory cortex (S1) and primary motor cortex (M1) neuronal
ensembles. In this paper, the activity modulations in hundreds of simultaneously recorded neurons
were analyzed, and it was demonstrated that both M1 and S1 neurons modulated their firing rate in
relation to the gait cycle. Remarkably, the firing rate of each neuron peaked at a particular phase
of the stepping cycle. Using a set of linear decoders (Wiener filters), the authors could thus predict
locomotion kinematic parameters with a very satisfying accuracy. Large neuronal ensembles were
needed for accurate predictions of leg kinematics, and the number of units required increased with
the task complexity. Furthermore, the authors report a superior performance of neuronal populations
drawn from several cortical areas in predicting movement kinematics compared to the performance
of populations drawn from a single area. Moreover, results indicated that both M1 and S1 neurons
contributed significantly to the prediction of the leg kinematics. As expected, M1 modulations were
more useful for predicting future values in the parameters of walking, whereas S1 modulations better
predicted the past values. This observation, however, must still be confirmed with more experiments,
since accurate predictions of future values of locomotion parameters could be obtained from S1 activity
in one of the two monkeys participating in the study. Nevertheless, this work provides the first proof of

Brain Sci. 2014,427
concept that, in the future, real-time neuroprosthetic systems for restoring bipedal walking in severely
paralyzed patients could be implemented.
Interesting results were also obtained with electrocorticography. Significant coherence between
right sensorimotor cortex and distal left leg muscles was found up to 60 Hz during voluntary induced
myoclonic jerks. Additional higher frequency coherence (∼140 and 190 Hz) was found during
sensory-induced myoclonic jerks [135]. Recently, significant decreases (4–7, 8–14 and 15–25 Hz) or
increases (26–45 and 65–95 Hz) in power (compared to the rest) were reported during spontaneous
movement of the hand and/or arm contralateral to electrode grid placement [136]. Furthermore, specific
high gamma ECoG responses were also identified during natural expressive speech and natural motor
tasks involving upper and lower extremities [137]. Thanks to these particular features, several research
teams have demonstrated that prosthesis control based on ECoG signals is quite feasible [138–141].
Nevertheless, these promising advances have been made for upper limb applications. It remains to be
shown if the same principles can be successfully exploited for developing walk rehabilitation systems.
Figure 8. Time-frequency plots (wavelet transformation) of LFP oscillations during gait
cycle. Upper row (A) and (B): analyzed electrode pair. The right electrode pair is on the right
side. (C) and (D): goniometer traces. Modulation of LFPs occurs in the 6–11 Hz frequency
range. In this frequency band, amplitudes are upregulated during the early stance phase and
swing phase of the contralateral leg. LL: left leg; RL: right leg; Gonio: goniometer; Flex:
flexion. This figure is reproduced with permission from [142]; Copyright Elsevier, 2011.
The last original invasive study that is worth mentioning in this review is the first analysis of
basal ganglia activity recorded from deep brain stimulation (DBS) electrodes in human subjects during
treadmill walking [142]. Recordings were made with patients who have DBS electrodes located in the
globus pallidus internum (GPi) for treatment of neck and upper trunk motor impairments, with no gait
disturbances. The authors report that local field potentials (LFP) spectra of GPi recordings made during
walking showed significantly higher power values in the lower frequency bands (4–12 Hz) and in the
gamma band (60–90 Hz) as compared to during sitting or standing. The opposite was seen in the beta

Brain Sci. 2014,428
band (15–25 Hz), where the power was significantly reduced during walking. According to the authors,
these changes may initiate or sustain gait-related activity in locomotor brainstem centers. No significant
differences over all frequency bands were observed between the sitting and the standing conditions.
Additionally, a modulation of the amplitudes in the theta-alpha (6–11 Hz) range was seen in all subjects.
The maximum amplitude variation was located between 6 and 11 Hz during the early stance phase of the
left leg in the right hemisphere and symmetrically on the left during the early stance phase of the right
leg (cf. Figure 8). This modulation seems to indicate that information about individual gait cycles is also
present in the basal ganglia.
4. Development of Non-Invasive Brain-Computer Interfaces Dedicated to Rehabilitation Systems
After reviewing the main principles of human locomotion control and, in particular, the spatial and
temporal characteristics of the supra-spinal control, the next paragraphs will deal with the development of
rehabilitation systems based on non-invasive brain-computer interfaces (BCI). After defining the concept
of BCI, different challenges, like detecting the movement intention based on EEG signals or translating
EEG signals to valuable commands dedicated to rehabilitation systems, will be reviewed and discussed.
4.1. General Considerations about BCIs
Brain-computer interfaces (BCI) include devices or systems that respond to neural or cognitive
processes. These systems enable their users, whose neural system may have been destroyed
by amputation, trauma or disease, to control a computer or any robotic device by interpreting
neurophysiological signals, which are recorded and processed following different steps, as shown in
Figure 9. First, the brain signals are pre-processed to clean them as much as possible. Then, some
features are extracted and classified so that the computer can determine in which mental state the user
was. Finally, the corresponding action is produced by the system. As it is non-invasive, light and
relatively cheap, electroencephalography (EEG) is the most used acquisition technique to record cerebral
activity of the BCI users.
Figure 9. General scheme of a classical brain-computer interface (BCI): first of all, the
subject performs a specific mental task in order to produce a signal of interest in his brain;
then, this signal is acquired and generally pre-processed in order to get rid of different
artifacts. Afterwards, some discriminating features are extracted and classified (pattern
recognition) to determine which specific signal was produced. Finally, the identified signal
is associated with a specific action to be performed by a computer or any electronic device.

Brain Sci. 2014,429
Thanks to current BCI technology, severely disabled people can communicate [143], control
computers [144] or drive robotic [145] or simple prosthetic devices [146] via the power of their brain
only, without activating any muscles. Nowadays, BCI applications dedicated to both disabled and healthy
users are also being developed in the video game field [147]. Although functional, BCI technology
offers information transfer rates that are too limited to control complex systems entirely. Consequently,
shared control is used extensively in assistive applications. This means that the BCI user generally sends
high-level commands to the system, which is able to operate all the low-level problems [1,148]. Very
interestingly, the benefits of BCI are not limited to the control of simple electric devices. It has been
shown that the simple fact of learning to operate a BCI has a positive impact on brain plasticity, with a
significant increase in motor cortical excitability and a modification of the brain network topology [149].
As such, BCI could become a viable tool for new post-stroke rehabilitation strategies [150].
In the case of BCI dedicated to walk rehabilitation systems, the first challenge is to detect the user
intention (to start walking, to stop, to go faster, to slow down, to turn left or right, etc.). Then, the system
has to generate a realistic human walking movement, corresponding to the detected user intent. Finally,
a feedback should be sent to the user, to help him control the system. These are the main challenges to
raise. They are detailed in the following sections, with an emphasis on existing systems and the latest
results obtained.
4.2. Detecting the User Intent
Detecting the user intent, and, particularly, predicting the next movement onset, type and direction,
is of first interest, as it could activate rehabilitation devices in a fully natural way. Although very few
gait intent detection studies have been undertaken [151,152], predicting the leg movement is of primary
importance and would definitely be helpful to adequately control an orthosis/prosthesis. Indeed, from a
mechanical point of view, the only interest of using brain signals to control a prosthesis would be to detect
early willingness for the next move [153]. Thereby, the whole device could anticipate the movement and
adjust the mechanical system as precisely as possible. It has to be underlined that this anticipation is not
feasible solely on the basis of physical sensors placed on the rehabilitation system, since these detect the
movement only once it has been produced.
From a practical point of view, researchers have basically two options to detect the movement
intention thanks to EEG signals: look for the emergence of a Bereitschaftspotential (BP) or detect the
production of an event-related (de-)synchronization (ERD/ERS) by the brain. The BP, on the one hand,
is a slow cortical potential that deepens in negativity about 1.5 s to 1 s before the movement onset.
As shown in Figure 10, the BP potential can be divided into three different parts. First, in the pre-BP
section, the brain signals are not affected by the movement intent yet. Then, in the BP section, or early
BP, the potential slowly decreases about 1.5 s before movement onset and is more prominent in the
central-medial areas. Second, the negative slope (NS), or late BP, corresponds to a steeper slope and
starts around 400 ms prior to movement onset. This potential is mainly localized over the primary motor
cortex (M1). It was shown that BP potentials are also observed in the brain just before the imagination
of movements [155]. In a recent study [108], the BP signal was analyzed during gait intent. Five
different tasks were performed: gait/stepping forward, gait and stepping backward and stepping laterally.

Brain Sci. 2014,430
As shown in Figure 11, the measured potentials vary slightly, depending on the experimental paradigm.
The most important potentials and the most notable variations occur at the top of the head, around
electrode Cz, which corresponds to the leg representation in the motor and sensorimotor cortices.
Figure 10. When looking at a Bereitschaftspotential (BP) signal, three main sections are
observed: no potential, a slow decreasing potential early BP and a steeper late BP (this
figure is reproduced with permission from [154]; Copyright Elsevier, 2005). MRCP is the
movement-related cortical potential.
Figure 11. It clearly appears that the BP potentials are similar for all five tasks. A
classification between those tasks would be difficult. The potentials are strong over the
motor cortex area close to the midline (MS is the movement start). This figure is reproduced
with permission from [108]; Copyright Elsevier, 2005.

Brain Sci. 2014,431
The event-related desynchronization, on the other hand, manifests itself as a short-lasting decrease of
power in some specific frequency bands [151]. This decrease can start as early as 2 s before movement
onset. Generally, the ERDs occur in the µ(8–12 Hz) and β(12–30 Hz) bands, which are directly linked
to movement planning and execution. These frequency limits , as well as the strength and laterality of
the ERD patterns depend on the subject, especially if the patient suffers from stroke impairment [156].
As reviewed in [151], several researchers have succeeded in automatically detecting BP or ERD,
in a non-invasive way. While most of the studies focus on the upper limb (finger, wrist, arm), only three
studies tackle the detection of ankle dorsiflexion movement without cues [114,157]. The obtained true
positive rates range between 82.5% [157] and 100% [114]. Another study evaluated the possibility to
classify right-hand, left-hand, tongue or right-foot movements with less success [158]. Very recently, the
single-trial classification of gait and point movement preparation was investigated [152]. Depending on
the analyzed pair of tasks, the average error rate was 25%.
Finally, motor-imagery was also investigated as an effective BCI paradigm to detect the intention to
start walking. It was shown in [159] that paraplegic and tetraplegic patients could trigger a walking
simulator by imagining themselves walking or idling. In these two experimental conditions, the EEG
power in the 9–13 Hz band in the mid-frontal (FCz), central (Cz) and central-parietal (CPz) areas
contained the best discriminant information. Based on these features, classification results estimated
offline ranged from about 60% to 90%. This proves that SCI patients have the possibility to operate
a robust BCI walking simulator with a short training period and satisfying accuracy. This expands the
results of a similar analysis that had been undertaken earlier [145]. All these studies are summarized
in Table 5.
Table 5. Several studies of EEG signals preceding lower limb motor tasks are available. The
presented results show that it should be feasible to activate a prosthetic/rehabilitation device.
SCI, spinal cord injury.
Publication Acquisition Protocol Brain Feature Key Findings
Niazi et al., 2011 [157]Ankle dorsiflexion
movement without cues BP
Predicted the movement with an
average true positive rate of 82.5%
around 187 ms before movement onset
Morash et al., 2008 [158]
Perform or imagine right-hand,
left-hand, tongue or right-foot
movement after a “Go” cue.
ERD/ERS
Predicting which of the four
movements/imageries is about to occur
is possible (around 40% accuracy)
Velu et al., 2013 [152]
Natural walking from a starting
position to a designated ending
position, pointing at a designated
position from the starting position
or remaining standing at the
starting position.
BP
Significant classification achieved
for all conditions, with errors for
movement vs. standing around 25%
when averaged across nine subjects
Do et al., 2011 [114]
Do et al., 2012 [160]
Detection of EEG patterns related
to repetitive foot dorsiflexions Activations in the Cz electrode
Foot lifter rehabilitation system based
on FES (online performance near 100%
using FFT-based features)
King et al., 2013 [159]
Motor imagery of walking or
idling executed by participants
with paraplegia or tetraplegia
due to SCI
ERD/ERS
The EEG power in the 9–13 Hz band in
the mid-frontal (FCz), central (Cz) and
central-parietal (CPz) areas were the
most different, comparing walking and
idling imagery; classification accuracy
from 60 to 90%
Leeb et al., 2007 [145]One tetraplegic patient imagines
movements of his paralyzed feet
Detection of βbursts
during imagery
Control of a wheelchair in a virtual
environment (go/stop). Classification
results ranging from 90 to 100%

Brain Sci. 2014,432
4.3. Developing a Direct Brain Signal to Limb Kinematics Decoding
Once the movement intent is detected, the rehabilitation system should produce realistic movements
of the lower limbs. Ideally, these movements should be produced according to the patient’s needs and/or
wishes, in order to render the locomotion process almost instinctive, like in real walking. To this end,
direct translation of the brain signals into limb kinematics is desirable. Such results, as previously
mentioned, have been obtained with monkeys on the basis of invasive experiments, in the context of
bipedal walking [1], direct cortical control of 3D neuroprosthetic devices [161] and cortical control of a
prosthetic arm for self-feeding [162]. Several studies analyze this approach as summarized in Table 6.
Table 6. Only one study suggests a direct decoding of brain signals into lower limb
kinematics. However, it was severely called into question by [121].
Publication BCI Paradigm Rehabilitation Device
or Technique Remarks
Presacco et al., 2011 [118]EEG to kinematics
translation None
Tries to reproduce results obtained by invasive
studies (the RMSvalue between prediction and
measurement is around 0.68); highly criticized
by [121]
Wagner et al., 2012 [127] EEG Lokomat
No control: evaluation of level of participation
in robotic-assisted treadmill walking (Fz and Pz
difference between active and passive walk)
Tanaka et al., 2013 [163] NIRS Exoskeleton
Body motion support-type mobile suit
Comparison of the cerebral activity during
walking using the suit and normal gait without
the suit; recommendations: most effective for
gait training to actually walk and not stay fixed
in one location; important for patients to swing
their arms during gait training in rehabilitation
An attempt to reproduce these results with non-invasive signals (EEG) has been made with human
subjects [118], as reported earlier in this review, but seems to be unfruitful, according to the arguments
developed in [121]. In the same underlying philosophy, robotic-assisted gait analysis has shown a
difference between active and passive walking around the Pz and Cz electrodes [127]. By automatically
detecting these modifications, a monitoring of the brain activation could be performed and determine the
best rehabilitation procedure, which could help one to recover more quickly. For instance, after analyzing
normal gait with and without a support exoskeleton in [163], the authors suggest that efficient lower limb
rehabilitation should be based on actual walking and not staying fixed in one location. Additionally, they
reported that it is important for patients to swing their arms during gait training in rehabilitation. Anyway,
future investigations will be needed to improve the procedure and verify, after all, if the recorded signals
contain, or not, sufficient information to control a prosthetic/rehabilitation device permanently. In the
meanwhile, other strategies have been developed to achieve a similar goal.
4.4. Producing a Gait/Stepping Movement in a Step by Step Approach
The alternative strategy to direct translation of EEG signals into walk kinematics signals consists of
detecting high level commands from EEG (like accelerate, decelerate, turn left, right, etc.), in order
to subsequently produce the desired movement using a dedicated algorithm [164,165]. Generally,
this algorithm will drive a functional electrical stimulation (FES) system, a prosthesis, orthosis or

Brain Sci. 2014,433
exoskeleton [166,167]. To detect high level commands from brain signals, several BCI paradigms can be
exploited using visual stimuli in order to elicit P300 potentials or steady-state visually evoked potentials
(SSVEPs) (see [65] for a review on BCI). Tactile stimulation could also be exploited to this aim [168].
To convert high level commands into kinematics, a class of algorithms called programmable central
pattern generators (PCPGs) is particularly suited for rehabilitation systems dedicated to walking.
Inspired by biological CPG, the PCPG algorithm is able to learn practically any periodical pattern (a
gait pattern, for instance) and automatically reproduce it [169]. Very advantageously, the amplitude and
frequency of the patterns produced may be changed on the fly, without producing any abrupt change in
the output command signal sent to the exoskeleton actuators. By analyzing the gait parameters of healthy
subjects walking on a treadmill, our research group showed that adequately combining the amplitude
and frequency of the PCPG allowed us to produce realistic gait kinematic patterns in a range of walking
speeds from 1.5 to 6 km/h [164].
Actually, only a few studies were performed under an ambulatory context. After a pilot study
investigating the feasibility of mobile auditory P300 detection [170], it was more precisely shown that the
P300 potential is not affected that much while walking [74,81,120]. Thus, although slow, this approach
obtained strongly reliable performance showing the feasibility of the link of a BCI with an external foot
lifter orthosis [171]. Based on a SSVEP paradigm, a preliminary study scanning the brain response from
10 Hz to 46 Hz has shown that walking is not a transparent process [172]. In another study using two
flickering stimuli, it was shown that ICA and canonical correlation analysis (CCA) were able to extract
an SSVEP response while walking [173].
When considering studies in the context of lower-limb rehabilitation, some other papers are available.
Using an SSVEP paradigm, it was shown that a lower limb exoskeleton could be controlled by both a step
by step approach and in a continuous way [174]. In [175], a walk rehabilitation system that recognizes
five types of intention is proposed and an 80% accuracy is claimed. In [176], motor imagery-based BCI
allows the subject to make a humanoid robot turn right, turn left and walk forward. The most important
results are summarized in Table 7.
Table 7. A few applications. SSVEP, steady-state visually evoked potential; PCPG,
programmable central pattern generator.
Publication BCI Paradigm Rehabilitation Device
or Technique Remarks
Cheron et al., 2012 [165] EEG (BCI) Exoskeleton (LOPES)/foot
lifter orthosis
Results from the Mindwalker and Bio-
fact projects
Mc Daid et al., 2013 [174] EEG (SSVEP)
Control of a lower limb exoskeleton;
studies motion intent detection and
continuous control by the BCI
The control appears feasible
Duvinage et al., 2013 [172] EEG (SSVEP) Scanning of the brain response from
10 Hz to 46 Hz Gait influences the SSVEP brain response
Duvinage et al., 2012 [171] EEG (P300) plus PCPG Foot lifter orthosis Reliable proof of concept
Li et al., 2012 [176]Humanoid robot control
thanks to BCI None
Identification of mental activities when
the subject is thinking “turning right,”
“turning left” or “walking forward”
Zhang et al., 2011 [175] EEG (SSVEP) plus CPG None
Walk rehabilitation system
that recognizes five types of intention
related to human walking; successful
classification accuracy above 80%;
functional using online EEG data

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5. Discussion and Considerations for Future Work
Recently, several studies dedicated to rehabilitation purposes have emerged in the non-invasive BCI
field. It appears that investigations for upper limbs are more often realized than for lower limbs. This
is probably due to both the inherent experimental difficulty of measuring EEG signals in the ambulatory
context and the challenging goal of balance control in walk rehabilitation tasks. Another reason is that
walking may be considered an automatic movement merely based on reflexes governed at the spinal
level. However, it has been recently confirmed that the motor cortex is particularly active during specific
phases of the gait cycle, particularly before the foot comes in contact with the ground [177]. Tremendous
progress in understanding human locomotion control has been made thanks to invasive studies and
neuroimaging techniques, giving the opportunity to build a bridge from BCI to new rehabilitation
systems and strategies.
Indeed, traditional approaches aiming at walk recovery after stroke, for instance, may be considered
as bottom-up approaches: they focus on the physical level (bottom) in order to influence the neural
system (top), being able to rehabilitate the patients because of the mechanisms of neural plasticity. How
these mechanisms are established is not well understood. Recently, some authors have promoted the
top-down approach, which consists of defining rehabilitation therapies based on the state of the brain
after stroke [178]. Mental simulation of movement engages the primary motor cortex in a similar
way that motor execution does. This mental exercise, called motor imagery (MI), induces distinctive
modulations of sensorimotor rhythms, which can be detected online. A specific BCI technology based on
such an MI paradigm can thus be used to help patients in cognitively rehearsing their physical skills in a
safe, repetitive manner, even in the case of no residual motor function [178]. This is an example in which
BCIs are extensively exploited to promote neuroplasticity in combination with traditional physiotherapy
and robot-aided therapy.
Nevertheless, before developing fully efficient neuronal prosthesis control systems, several
challenging problems remain to be solved. These have already been mentioned throughout this review.
They are summarized below.
According to the recent results presented in the previous sections, it seems quite possible to
successfully detect the intention of walking on the basis of EEG signals only (go/no-go detection) [151].
However, determining the precise orientation of the movement by analyzing the shape of the
Bereitschaftspotential happens to be much more challenging, given the weak difference between the
BP associated with forward and backward movements [108].
Successful conversion of brain signals into limb kinematics has been achieved with monkeys, on
the basis of invasive measurements [1]. Recently, an attempt to realize a similar conversion with EEG
signals in humans has been made [118], but the statistical significance of the obtained results remains to
be clearly established [121]. In particular, the question arises if the effective bit rate that can be achieved
with EEG technology is sufficient to constantly drive a robotic arm or leg.
By contrast, non-invasive EEG signals can be effectively exploited to run standard brain-computer
interfaces. These BCI can be designed to decode high level orders (like, for instance, “go faster”,
“go slower”, “turn left”, “stop”, etc.), while adapted algorithms, such as PCPG, provide the rehabilitation
system actuators with meaningful limb kinematics [164,165]. It was shown in several publications that

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standard BCI paradigm performances are not much affected by the numerous artifacts polluting EEG
signals recorded under “gentle” ambulatory conditions [74,170]. The use of external visual stimuli (with
P300 or SSVEP BCI paradigms) may, however, be considered cumbersome and too tiring by patients.
In order to develop better BCIs for walk rehabilitation, fundamental research aiming at detecting the
precise role of motor cortex during the gait cycle (and other movements, like stair climbing) should be
pursued. It is indeed important to figure out what can effectively and non-ambiguously be measured
using EEG: descending commands from the motor cortex; integration of ascending sensorimotor
information; artifacts (probably a mix of these different contributions, but in which proportions?). In
this context, it seems necessary to define several experimental protocols in order to disentangle the
different signals.
To this end, we propose characterizing the descending brain commands that are involved in human
walk control in a static approach (inspired by [112]), in order to ensure the absence of EEG motion
artifacts. To this aim, the EEG signals of subjects sitting on a chair would be recorded. The subjects
would then be asked to produce voluntary rhythmic foot movements, staying at the same tempo. The
feet will not be in contact with the ground, to ensure a minimal sensorimotor feedback. Several tempos
would be produced. Furthermore, EEG should be recorded when the subject is sitting and not moving
the feet, to define a baseline, necessary when using brain imagery tools, like LORETA[179]. To assess
the presence (or absence) of motion artifacts, an accelerometer should be placed on the neck. A complete
characterization of these data could then be realized, by analyzing the event-related spectral perturbations
(ERSP) combined with a time-warping transformation [117], by computing directed corticomuscular
coherence and, in particular, delays between EEG and EMG time series (to assess the information
flow direction). Then, characterization of EEG signals caused by somatosensory information coming
from the feet of the subject when sitting (again, to prevent any motion artifacts) should be undertaken.
More precisely, the same experiment as above could be realized, with the feet in contact with ground,
this time. By comparing the two states (contact/no contact), it would be possible to emphasize the
contribution of sensory feedback. Alternatively, one could use special tactors to stimulate the feet,
mimicking the sensation of walk and study the properties of the EEG signals that are phase-locked with
this stimulation. Finally, if motion artifacts are correctly rejected, provided we know the signals due
to descending commands (voluntary rhythmic movements) and those due to tactile stimulation (tactors,
mimicking the sensation of walking), we should be able to disentangle the contribution of posture and
balance control when the subject is standing and walking.
Directed coherence (Granger causality) is a promising way to disentangle descending from ascending
contributions in EEG signals. Unlike coherence, which measures correlation and, therefore, does not
allow one to identify the direction of interaction between two signals, Granger causality can provide
information about possible causal relationships. Using such an analysis, directed coherence in both the
descending (EEG to EMG) and ascending (EMG to EEG) directions was found in beta frequencies, in
human subjects performing a precision grip task [180]. This study provides, for the first time, clear
evidence of bidirectional corticomuscular coherence in man. Such an analysis should also be made in
the context of bipedal walking.
Of course, another crucial research axis to further develop deals with the adequate cleaning of
motion artifacts in EEG signals. The first available option consists of mathematically correcting

Brain Sci. 2014,436
corrupted signals. The most popular method is independent component analysis (ICA), which consists
of separating EEG into subcomponents (independent components) in such a way that these are all
statistically independent of each other. By discarding components related to artifacts, it is possible
to reconstruct cleaned EEG signals [69]. While this technique is really powerful to eliminate basic
artifacts (like eye motion or blink artifacts), it seems not so well adapted to motion artifacts produced
during walking, since these cannot be completely separated from cortical signals, as explained in [120].
Furthermore, the weak point of this technique resides in the arbitrary nature of the choice of independent
components to eliminate. Therefore, improved cleaning methods are desirable, especially if the goal is
to directly convert EEG signals into lower limb kinematics. On the other hand, it has been demonstrated
that motion artifacts do not dramatically impact low complexity P300-based BCI under ambulatory
conditions [74], for instance. Interestingly, it has been shown that artifact-resistant measures could
be computed in order to detect cognitive EEG activity during locomotion [119]. This may constitute a
useful perspective for the future. Finally, one could also mention the idea of developing a device able
to determine the motion artifacts corrupting the EEG electrode online, in order to correct them with
adaptive filtering by optimal projection [181].
From a fundamental point of view, we would like to underline the fact that the mechanism of gait
control may change as a function of the walking speed, since the kinematics and EMG patterns during the
gait cycle vary significantly in shape under 3 km/h [17]. Thus, it should be interesting to systematically
compare EEG signals during walking at very low, normal and high speeds.
To conclude, it is really exciting to see that new BCI applications are being developed in the field
of rehabilitation. These realizations are based on the considerable knowledge acquired over time in the
field of brain sciences. In this review, we have summarized the main principles of human locomotion
control, described the first non-invasive BCIs dedicated to walk rehabilitation systems and identified the
main technical challenges ahead in the field. We must also not forget that the patients should always be
put at the heart of the development process [182], by integrating their personal needs and preferences, in
order to produce the best possible benefit for their rehabilitation.
Acknowledgments
The authors would like to thank the reviewers for their valuable comments.
M. Duvinage is an FNRS (Fonds National de la Recherche Scientifique) Research Fellow.
This paper presents research results of the Belgian Network DYSCO (Dynamical Systems, Control
and Optimization), funded by the Interuniversity Attraction Poles Programme, initiated by the Belgian
State, Science Policy Office. The scientific responsibility rests with its author(s).
Conflicts of Interest
The authors declare no conflict of interest.

Brain Sci. 2014,437
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