Pure phase-locking of beta/gamma oscillation contributes to the N30 frontal component of somatosensory evoked potentials

BACKGROUND: Evoked potentials have been proposed to result from phase-locking of electroencephalographic (EEG) activities within specific frequency bands. However, the respective contribution of phasic activity and phase resetting of ongoing EEG oscillation remains largely debated. We here applied the EEGlab procedure in order to quantify the contribution of electroencephalographic oscillation in the generation of the frontal N30 component of the somatosensory evoked potentials (SEP) triggered by median nerve electrical stimulation at the wrist. Power spectrum and intertrial coherence analysis were performed on EEG recordings in relation to median nerve stimulation. RESULTS: The frontal N30 component was accompanied by a significant phase-locking of beta/gamma oscillation (25-35 Hz) and to a lesser extent of 80 Hz oscillation. After the selection in each subject of the trials for which the power spectrum amplitude remained unchanged, we found pure phase-locking of beta/gamma oscillation (25-35 Hz) peaking about 30 ms after the stimulation. Transition across trials from uniform to normal phase distribution revealed temporal phase reorganization of ongoing 30 Hz EEG oscillations in relation to stimulation. In a proportion of trials, this phase-locking was accompanied by a spectral power increase peaking in the 30 Hz frequency band. This corresponds to the complex situation of 'phase-locking with enhancement' in which the distinction between the contribution of phasic neural event versus EEG phase resetting is hazardous. CONCLUSION: The identification of a pure phase-locking in a large proportion of the SEP trials reinforces the contribution of the oscillatory model for the physiological correlates of the frontal N30. This may imply that ongoing EEG rhythms, such as beta/gamma oscillation, are involved in somatosensory information processing.Comparative StudyJournal ArticleResearch Support, Non-U.S. Gov'tinfo:eu-repo/semantics/publishe

Reproducibility of the identification process of stump muscle EMG in prosthetic gait by a dynamic recurrent neural network

Supp 1:181info:eu-repo/semantics/publishe

Strategies of locomotor control in cerebral palsy

info:eu-repo/semantics/publishe

Development and motor control :from the first step on

AbstractFor performing their very first unsupported steps, often considered as a ‘milestone’ event in locomotor development, toddlers must find a compromise between at least two requirements: (1) the postural stability of the erect posture integrating the direction of gravity and (2) the dynamic control of the body and limbs for forward progression these two aspects. In adults, a series of experimental studies have provided evidence for coordinative laws that lead to a reduction of kinematic degrees of freedom. When the elevation angles of the thigh, shank and foot are plotted one versus the others, they describe a regular gait loop which lies to a plane. The plane orientation and the loop shape reflect the phase relationship between the different segments and therefore the timing of intersegmental coordination. The general pattern of intersegmental coordination and the stabilization of the trunk with respect of vertical are immature at the onset of unsupported walking in toddlers, but they develop in parallel very rapidly in the first few weeks of walking experience. Adult-like cross-correlation function parameters were reached earlier for shank-foot pairs than for thigh-shank indicating disto-proximal maturation of the lower limb segments coordination. We also demonstrated that a dynamic recurrent neural network (DRNN) is able to reproduce lower limb kinematics in toddler locomotion by using multiple raw EMG data. In the context of motor learning the DRNN may be considered as a model of biological learning mechanisms underlying motor adaptation. Using this artificial learning during the very first steps we found that the attractor states reached through learning correspond to biologically interpretable solutions.info:eu-repo/semantics/publishe

Recognition of the physiological actions of the triphasic EMG pattern by a dynamic recurrent neural network.

Triphasic electromyographic (EMG) patterns with a sequence of activity in agonist (AG1), antagonist (ANT) and again in agonist (AG2) muscles are characteristic of ballistic movements. They have been studied in terms of rectangular pulse-width or pulse-height modulation. In order to take into account the complexity of the EMG signal within the bursts, we used a dynamic recurrent neural network (DRNN) for the identification of this pattern in subjects performing fast elbow flexion movements. Biceps and triceps EMGs were fed to all 35 fully-connected hidden units of the DRNN for mapping onto elbow angular acceleration signals. DRNN training was supervised, involving learning rule adaptations of synaptic weights and time constants of each unit. We demonstrated that the DRNN is able to perfectly reproduce the acceleration profile of the ballistic movements. Then we tested the physiological plausibility of all the networks that reached an error level below 0.001 by selectively increasing the amplitude of each burst of the triphasic pattern and evaluating the effects on the simulated accelerating profile. Nineteen percent of these simulations reproduced the physiological action classically attributed to the 3 EMG bursts: AG1 increase showed an increase of the first accelerating pulse, ANT an increase of the braking pulse and AG2 an increase of the clamping pulse. These networks also recognized the physiological function of the time interval between AG1 and ANT, reproducing the linear relationship between time interval and movement amplitude. This task-dynamics recognition has implications for the development of DRNN as diagnostic tools and prosthetic controllers.Journal ArticleResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishe

Development and motor control: from the first steps on

For performing their very first unsupported steps, often considered as a 'milestone' event in locomotor development, toddlers must find a compromise between at least two requirements: (1) the postural stability of the erect posture integrating the direction of gravity and (2) the dynamic control of the body and limbs for forward progression these two aspects. In adults, a series of experimental studies have provided evidence for coordinative laws that lead to a reduction of kinematic degrees of freedom. When the elevation angles of the thigh, shank and foot are plotted one versus the others, they describe a regular gait loop which lies to a plane. The plane orientation and the loop shape reflect the phase relationship between the different segments and therefore the timing of intersegmental coordination. The general pattern of intersegmental coordination and the stabilization of the trunk with respect of vertical are immature at the onset of unsupported walking in toddlers, but they develop in parallel very rapidly in the first few weeks of walking experience. Adult-like cross-correlation function parameters were reached earlier for shank-foot pairs than for thigh-shank indicating disto-proximal maturation of the lower limb segments coordination. We also demonstrated that a dynamic recurrent neural network (DRNN) is able to reproduce lower limb kinematics in toddler locomotion by using multiple raw EMG data. In the context of motor learning the DRNN may be considered as a model of biological learning mechanisms underlying motor adaptation. Using this artificial learning during the very first steps we found that the attractor states reached through learning correspond to biologically interpretable solutions. © 2006 Springer Science+Business Media, Inc.SCOPUS: ch.binfo:eu-repo/semantics/publishe

A dynamic recurrent neural network for multiple muscles electromyographic mapping to elevation angles of the lower limb in human locomotion.

This paper describes the use of a dynamic recurrent neural network (DRNN) for simulating lower limb coordination in human locomotion. The method is based on mapping between the electromyographic signals (EMG) from six muscles and the elevation angles of the three main lower limb segments (thigh, shank and foot). The DRNN is a fully connected network of 35 hidden units taking into account the temporal relationships history between EMG and lower limb kinematics. Each EMG signal is sent to all 35 units, which converge to three outputs. Each output neurone provides the kinematics of one lower limb segment. The training is supervised, involving learning rule adaptations of synaptic weights and time constant of each unit. Kinematics of the locomotor movements were recorded and analysed using the opto-electronic ELITE system. Comparative analysis of the learning performance with different types of output (position, velocity and acceleration) showed that for common gait mapping velocity data should be used as output, as it is the best compromise between asymptotic error curve, rapid convergence and avoidance of bifurcation. Reproducibility of the identification process and biological plausibility were high, indicating that the DRNN may be used for understanding functional relationships between multiple EMG and locomotion. The DRNN might also be of benefit for prosthetic control.Comparative StudyJournal ArticleResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishe

Is there a relation between metabolic energy expenditure and individual kinematic adaptation to changes in walking speed in normal adults?

SCOPUS: cp.jinfo:eu-repo/semantics/publishe

Planar covariation of elevation angles in prosthetic gait.

In order to achieve efficacious walking, transfemoral amputees must adapt coordination within both the artificial and the sound lower limb. We analyzed kinematic strategies in amputees using the planar covariation of lower limb segments approach. When the elevation angles of the thigh, shank and foot are plotted one versus the others, they describe a regular loop which lies close to a plane in normal adults' gait. Orientation of this plane changes with increased speed, in relation to mechanical energetic saving. We used an opto-electronic device to record the elevation angles of both limbs' segments of novice and expert transfemoral amputees and compared them to those of control subjects. The statistical structure underlying the distribution of these angles was described by principal component analysis and Fourier transform. The typical elliptic loop was preserved in prosthetic walking, in both limbs in both novice and expert transfemoral amputees. This reflects a specific control over the thigh elevation angle taking into account knowledge of the other elevation angles throughout the gait cycle. The best-fitting plane of faster trials rotates around the long axis of the gait loop with respect to the plane of slower trials for control subjects, and even more for the sound limb of expert amputees. In contrast, plane rotation is very weak or absent for the prosthetic limb. We suggest that these results reveal a centrally commanded compensation strategy.JOURNAL ARTICLESCOPUS: ar.jinfo:eu-repo/semantics/publishe

Multi-segment coordination strategies in choregraphic jump «grand jeté»

SCOPUS: cp.jinfo:eu-repo/semantics/publishe