Understanding the processes underlying sensorimotor coupling with the environment is crucial for sensorimotor rehabilitation and sensory substitution. In doing so, devices which provide novel sensory feedback consequent to body movement may be optimized in order to enhance motor performance for particular tasks. The aim of the study reported here was to investigate audio-motor coupling when the auditory experience is linked to movements of the head or the hands. The participants had to localize and reach a virtual source with the dominant hand in response to sounds. An electromagnetic system recorded the position and orientation of the participants' head and hands. This system was connected to a 3D audio system that provided binaural auditory feedback on the position of the virtual listener located on the participants' body. The listener's position was computed either from the hands or from the head. For the hand condition, the virtual listener was placed on the dominant hand (the one used to reach the target) in Experiment 1 and on the non-dominant hand, which was constrained in order to have similar amplitude and degrees of freedom as that of the head, in Experiment 2. The results revealed that, in the two experiments, the participants were able to localize a source within the 3D auditory environment. Performance varied as a function of the effector's degrees of freedom and the spatial coincidence between sensor and effector. The results also allowed characterizing the kinematics of the hand and head and how they change with audio-motor coupling condition and practice.

It is not known whether, during the course of aging, changes occur in the motor strategies used by the CNS for lifting objects of different weights. Here, we analyzed the kinematics of object-lifting in two different healthy groups (young and elderly people) plus one well-known deafferented patient (GL). The task was to reach and lift onto a shelf an opaque cylindrical object with changing weight. The movements of the hand and object were recorded with electromagnetic sensors. In an ecological context (i.e. no instruction was given about movement speed), we found that younger participants, elderly people and GL did not all move at the same speed and that, surprisingly, elder people are faster. We also observed that the lifting trajectories were constant for both the elderly and the deafferented patient while younger participants raised their hand higher when the object weighed more. It appears that, depending on age and on available proprioceptive information, the CNS uses different strategies of lifting. We suggest that elder people tend to optimize their feedforward control in order to compensate for less functional afferent feedback, perhaps to optimize movement time and energy expenditure at the expense of high precision. In the case of complete loss of proprioceptive input, however, compensation follows a different strategy as suggested by GL's behavior who moved more slowly compared to both our younger and older participants.

Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of BEG and event-related potentials (ERP) in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu), and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding) in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG) and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators.

Powered exoskeletons can empower paraplegics to stand and walk. Actively controlled hip ab/adduction (HAA) is needed for weight shift and for lateral foot placement to support dynamic balance control and to counteract disturbances in the frontal plane. Here, we describe the design, control, and preliminary evaluation of a novel exoskeleton, MINDWALKER. Besides powered hip flexion/extension and knee flexion/extension, it also has powered HAA. Each of the powered joints has a series elastic actuator, which can deliver 100 Nm torque and 1 kW power. A finite-state machine based controller provides gait assistance in both the sagittal and frontal planes. State transitions, such as stepping, can be triggered by the displacement of the Center of Mass (CoM). A novel step-width adaptation algorithm was proposed to stabilize lateral balance. We tested this exoskeleton on both healthy subjects and paraplegics. Experimental results showed that all users could successfully trigger steps by CoM displacement. The step-width adaptation algorithm could actively counteract disturbances, such as pushes. With the current implementations, stable walking without crutches has been achieved for healthy subjects but not yet for SCI paraplegics. More research and development is needed to improve the gait stability.

A central question in Neuroscience is that of how the nervous system generates the spatiotemporal commands needed to realize complex gestures, such as handwriting. A key postulate is that the central nervous system (CNS) builds up complex movements from a set of simpler motor primitives or control modules. In this study we examined the control modules underlying the generation of muscle activations when performing different types of movement: discrete, point-to-point movements in eight different directions and continuous figure-eight movements in both the normal, upright orientation and rotated 90°. To test for the effects of biomechanical constraints, movements were performed in the frontal-parallel or sagittal planes, corresponding to two different nominal flexion/abduction postures of the shoulder. In all cases we measured limb kinematics and surface electromyographic activity (EMG) signals for seven different muscles acting around the shoulder. We first performed principal component analysis (PCA) of the EMG signals on a movement-by-movement basis. We found a surprisingly consistent pattern of muscle groupings across movement types and movement planes, although we could detect systematic differences between the PCs derived from movements performed in each shoulder posture and between the principal components associated with the different orientations of the figure. Unexpectedly we found no systematic differences between the figure eights and the point-to-point movements. The first three principal components could be associated with a general co-contraction of all seven muscles plus two patterns of reciprocal activation. From these results, we surmise that both "discrete-rhythmic movements" such as the figure eight, and discrete point-to-point movement may be constructed from three different fundamental modules, one regulating the impedance of the limb over the time span of the movement and two others operating to generate movement, one aligned with the vertical and the other aligned with the horizontal.

Gait is an essential human activity which organizes many functional and cognitive behaviors. The biomechanical constraints of bipedalism implicating a permanent control of balance during gait are taken into account by a complex dialog between the cortical, subcortical and spinal networks. This networking is largely based on oscillatory coding, including changes in spectral power and phase-locking of ongoing neural activity in theta, alpha, beta and gamma frequency bands. This coding is specifically modulated in actual gait execution and representation, as well as in contexts of gait observation or imagination. A main challenge in integrative neuroscience oscillatory activity analysis is to disentangle the brain oscillations devoted to gait control. In addition to neuroimaging approaches, which have highlighted the structural components of an extended network, dynamic high-density EEG gives non-invasive access to functioning of this network. Here we revisit the neurophysiological foundations of behavior-related EEG in the light of current neuropsychological theoretic frameworks. We review different EEG rhythms emerging in the most informative paradigms relating to human gait and implications for rehabilitation strategies.

During human walking, there exists a functional neural coupling between arms and legs, and between cervical and lumbosacral pattern generators. Here, we present a novel approach for associating the electromyographic (EMG) activity from upper limb muscles with leg kinematics. Our methodology takes advantage of the high involvement of shoulder muscles in most locomotor-related movements and of the natural co-ordination between arms and legs. Nine healthy subjects were asked to walk at different constant and variable speeds (3-5 km/h), while EMG activity of shoulder (deltoid) muscles and the kinematics of walking were recorded. To ensure a high level of EMG activity in deltoid, the subjects performed slightly larger arm swinging than they usually do. The temporal structure of the burst-like EMG activity was used to predict the spatiotemporal kinematic pattern of the forthcoming step. A comparison of actual and predicted stride leg kinematics showed a high degree of correspondence (r > 0.9). This algorithm has been also implemented in pilot experiments for controlling avatar walking in a virtual reality setup and an exoskeleton during over-ground stepping. The proposed approach may have important implications for the design of human machine interfaces and neuroprosthetic technologies such as those of assistive lower limb exoskeletons.

In this study we employed a dynamic recurrent neural network (DRNN) in a novel fashion to reveal characteristics of control modules underlying the generation of muscle activations when drawing figures with the outstretched arm. We asked healthy human subjects to perform four different figure-eight movements in each of two workspaces (frontal plane and sagittal plane). We then trained a DRNN to predict the movement of the wrist from information in the EMG signals from seven different muscles. We trained different instances of the same network on a single movement direction, on all four movement directions in a single movement plane, or on all eight possible movement patterns and looked at the ability of the DRNN to generalize and predict movements for trials that were not included in the training set. Within a single movement plane, a DRNN trained on one movement direction was not able to predict movements of the hand for trials in the other three directions, but a DRNN trained simultaneously on all four movement directions could generalize across movement directions within the same plane. Similarly, the DRNN was able to reproduce the kinematics of the hand for both movement planes, but only if it was trained on examples performed in each one. As we will discuss, these results indicate that there are important dynamical constraints on the mapping of EMG to hand movement that depend on both the time sequence of the movement and on the anatomical constraints of the musculoskeletal system. In a second step, we injected EMG signals constructed from different synergies derived by the PCA in order to identify the mechanical significance of each of these components. From these results, one can surmise that discrete-rhythmic movements may be constructed from three different fundamental modules, one regulating the co-activation of all muscles over the time span of the movement and two others elliciting patterns of reciprocal activation operating in orthogonal directions.

Neuroprosthetic technology and robotic exoskeletons are being developed to facilitate stepping, reduce muscle efforts and promote motor recovery. Nevertheless, the guidance forces of an exoskeleton may influence the sensory inputs, sensorimotor interactions and resulting muscle activity patterns during stepping. The aim of this study was to report the muscle activation patterns in a sample of intact and injured subjects while walking with a robotic exoskeleton and, in particular, to quantify the level of muscle activity during assisted gait. We recorded electromyographic (EMG) activity of different leg and arm muscles during overground walking in an exoskeleton in six healthy individuals and four spinal cord injury (SCI) participants. In SCI patients, EMG activity of the upper limb muscles was augmented while activation of leg muscles was typically small. Contrary to our expectations, however, in neurologically intact subjects, EMG activity of leg muscles was similar or even larger during exoskeleton-assisted walking compared to normal overground walking. In addition, significant variations in the EMG waveforms were found across different walking conditions. The most variable pattern was observed in the hamstring muscles. Overall, the results are consistent with a non-linear reorganization of the locomotor output when using the robotic stepping devices. The findings may contribute to our understanding of human-machine interactions and adaptation of locomotor activity patterns.

Biological motion observation has been recognized to produce dynamic change in sensorimotor activation according to the observed kinematics. Physical plausibility of the spatial-kinematic relationship of human movement may play a major role in the top-down processing of human motion recognition. Here, we investigated the time course of scalp activation during observation of human gait in order to extract and use it on future integrated brain-computer interface using virtual reality (VR). We analyzed event related potentials (ERP), the event related spectral perturbation (ERSP) and the inter-trial coherence (ITC) from high-density EEG recording during video display onset (-200-600 ms) and the steady state visual evoked potentials (SSVEP) inside the video of human walking 3D-animation in three conditions: Normal; Upside-down (inverted images); and Uncoordinated (pseudo-randomly mixed images). We found that early visual evoked response P120 was decreased in Upside-down condition. The N170 and P300b amplitudes were decreased in Uncoordinated condition. In Upside-down and Uncoordinated conditions, we found decreased alpha power and theta phase-locking. As regards gamma oscillation, power was increased during the Upside-down animation and decreased during the Uncoordinated animation. An SSVEP-like response oscillating at about 10 Hz was also described showing that the oscillating pattern is enhanced 300 ms after the heel strike event only in the Normal but not in the Upside-down condition. Our results are consistent with most of previous point-light display studies, further supporting possible use of virtual reality for neurofeedback applications.

BACKGROUND:For two decades, EEG-based Brain-Computer Interface (BCI) systems have been widely studied in research labs. Now, researchers want to consider out-of-the-lab applications and make this technology available to everybody. However, medical-grade EEG recording devices are still much too expensive for end-users, especially disabled people. Therefore, several low-cost alternatives have appeared on the market. The Emotiv Epoc headset is one of them. Although some previous work showed this device could suit the customer's needs in terms of performance, no quantitative classification-based assessments compared to a medical system are available.METHODS:This paper aims at statistically comparing a medical-grade system, the ANT device, and the Emotiv Epoc headset by determining their respective performances in a P300 BCI using the same electrodes. On top of that, a review of previous Emotiv studies and a discussion on practical considerations regarding both systems are proposed. Nine healthy subjects participated in this experiment during which the ANT and the Emotiv systems are used in two different conditions: sitting on a chair and walking on a treadmill at constant speed.RESULTS:The Emotiv headset performs significantly worse than the medical device; observed effect sizes vary from medium to large. The Emotiv headset has higher relative operational and maintenance costs than its medical-grade competitor.CONCLUSIONS:Although this low-cost headset is able to record EEG data in a satisfying manner, it should only be chosen for non critical applications such as games, communication systems, etc. For rehabilitation or prosthesis control, this lack of reliability may lead to serious consequences. For research purposes, the medical system should be chosen except if a lot of trials are available or when the Signal-to-Noise Ratio is high. This also suggests that the design of a specific low-cost EEG recording system for critical applications and research is still required.

Although ElectroOculoGraphic (EOG) signals have been intensively used for human-machine interfaces, none of the available eye movement recognition techniques have been objectively compared to each other. In this paper, we propose to compare two widely known techniques (the standard R. Barea (RB) and A. Bulling (AB)'s works) and a Spiking Neural Network based approach. We also suggest several potential improvements that were all assessed according to the Fl-score. Additionally, we investigate 3 different target configurations on the screen: 3×3, 3×5 and 5×5. This aims at detecting which configuration can reach the best bitrate. Finally, double blink and wink detectors are Fl-score evaluated to estimate their relevancy as a mouse click. In this 6-healthy-subject experiment, we observed that both RB and AB methods provide fairly similar results. According to the bitrate analysis while considering complexity, the 3×3 is the most suitable interface. Among the different potential enhancements, the clustering approach instead of a fixed grid leads to a much quicker learning procedure. Regarding the eye mouse click detectors, their performance should be high enough to be used in a reliable interface.

The existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum, or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for addressing this central question. The coordination of the elevation of the 3 leg segments under a planar covariation rule (Borghese et al., 1996) was recently modeled (Barliya et al., 2009) by phase-adjusted simple oscillators shedding new light on the understanding of the central pattern generator (CPG) processing relevant oscillation signals. We describe the use of a dynamic recurrent neural network (DRNN) mimicking the natural oscillatory behavior of human locomotion for reproducing the planar covariation rule in both legs at different walking speeds. Neural network learning was based on sinusoid signals integrating frequency and amplitude features of the first three harmonics of the sagittal elevation angles of the thigh, shank, and foot of each lower limb. We verified the biological plausibility of the neural networks. Best results were obtained with oscillations extracted from the first three harmonics in comparison to oscillations outside the harmonic frequency peaks. Physiological replication steadily increased with the number of neuronal units from 1 to 80, where similarity index reached 0.99. Analysis of synaptic weighting showed that the proportion of inhibitory connections consistently increased with the number of neuronal units in the DRNN. This emerging property in the artificial neural networks resonates with recent advances in neurophysiology of inhibitory neurons that are involved in central nervous system oscillatory activities. The main message of this study is that this type of DRNN may offer a useful model of physiological central pattern generator for gaining insights in basic research and developing clinical applications.

EEG-based systems have been the most widely used in the field of Brain-Computer Interfaces (BCI) for two decades. Plenty of applications have been proposed from games to rehabilitation systems. Until recently, EEG recording devices were too expensive for an end-user. Today, several low-cost alternatives have appeared on the market. The most sophisticated of these low-cost devices is the Emotiv Epoc headset. Some studies reported that this device is suitable for customers in terms of performance. However, none of the previous studies reported to what extent the Emotiv headset is working well compared to a medical system. The aim of this paper is thus to scientifically compare a medical system and the Emotiv Epoc headset by determining their respective performances in the context of a P300 BCI paradigm. In this study, seven healthy subjects performed P300 experiments and two different conditions were studied: sitting on a chair and walking on a treadmill at constant speed. Results show that the Emotiv headset, although able to record EEG data and not only artifacts, is sometimes significantly worse than a medical system. Those results suggest that the design of a specific low-cost EEG recording systems for rehabilitation purposes at a low price is still required.

Current lower limb prostheses do not integrate recent developments in robotics and in Brain-Computer Interfaces (BCIs). In fact, active lower limb prostheses seldom consider the user's intent, they often determine the correct movement from those of healthy parts of the body or from the residual limb. Recently, an emerging idea for non-invasive BCIs was proposed to allow such low bitrate systems to control a lower limb prosthesis thanks to a Central Pattern Generator (CPG) widely used in robotics. This CPG allows to automatically generate a periodic gait pattern. Furthermore, the CPG pattern frequency and magnitude can be adapted according to the specific gait behavior of the patient and his desired speed. This paper proves the concept of combining a human gait model based on a CPG and a classic but non-natural P300 BCI in order to consider the user's intent. The details of how the entire chain can be practically implemented are given. Finally, preliminary results on four healthy subjects for a four-speed P300-based lower limb orthosis with a non-control state are presented. Globally, results are satisfying and prove the feasibility of such systems.

This paper presents MINDWALKER, which is an ambitious EC funded research project coordinated by Space Applications Services aiming at the development of novel Brain Neural Computer Interfaces (BNCI) and robotics technologies, with the goal of obtaining a crutch-less assistive lower limbs exoskeleton, with non-invasive brain control approach as main strategy. Complementary BNCI control approaches such as arms electromyograms (EMG) are also researched. In the last phase of the project, the developed system should undergo a clinical evaluation with Spinal Cord Injured (SCI) subjects at the Fondazione Santa Lucia, Italy.

Success in locomotor rehabilitation programs can be improved with the use of brain-computer interfaces (BCIs). Although a wealth of research has demonstrated that locomotion is largely controlled by spinal mechanisms, the brain is of utmost importance in monitoring locomotor patterns and therefore contains information regarding central pattern generation functioning. In addition, there is also a tight coordination between the upper and lower limbs, which can also be useful in controlling locomotion. The current paper critically investigates different approaches that are applicable to this field: the use of electroencephalogram (EEG), upper limb electromyogram (EMG), or a hybrid of the two neurophysiological signals to control assistive exoskeletons used in locomotion based on programmable central pattern generators (PCPGs) or dynamic recurrent neural networks (DRNNs). Plantar surface tactile stimulation devices combined with virtual reality may provide the sensation of walking while in a supine position for use of training brain signals generated during locomotion. These methods may exploit mechanisms of brain plasticity and assist in the neurorehabilitation of gait in a variety of clinical conditions, including stroke, spinal trauma, multiple sclerosis, and cerebral palsy.

Recent research has shown that a P300 system can be used while walking without requiring any specific gait-related artifact removal techniques. Also, standard EEG-based Brain-Computer Interfaces (BCI) have not been really assessed for lower limb rehabilitation/prosthesis. Therefore, this paper gives a first baseline estimation (for future BCI comparisons) of the subjective and objective performances of a four-state P300 BCI plus a non-control state for lower-limb rehabilitation purposes. To assess usability and workload, the System Usability Scale and the NASA Task Load Index questionnaires were administered to five healthy subjects after performing a real-time treadmill speed control. Results show that the P300 BCI approach could suit fitness and rehabilitation applications, whereas prosthesis control, which suffers from a low reactivity, appears too sensitive for risky and crowded areas.