Moving to a different beat

Repeated Vowel Production Affects Features of Neural Activity in Sensorimotor Cortex

The sensorimotor cortex is responsible for the generation of movements and interest in the ability to use this area for decoding speech by brain–computer interfaces has increased recently. Speech decoding is challenging however, since the relationship between neural activity and motor actions is not completely understood. Non-linearity between neural activity and movement has been found for instance for simple finger movements. Despite equal motor output, neural activity amplitudes are affected by preceding movements and the time between movements. It is unknown if neural activity is also affected by preceding motor actions during speech. We addressed this issue, using electrocorticographic high frequency band (HFB; 75–135 Hz) power changes in the sensorimotor cortex during discrete vowel generation. Three subjects with temporarily implanted electrode grids produced the /i/ vowel at repetition rates of 1, 1.33 and 1.66 Hz. For every repetition, the HFB power amplitude was determined. During the first utterance, most electrodes showed a large HFB power peak, which decreased for subsequent utterances. This result could not be explained by differences in performance. With increasing duration between utterances, more electrodes showed an equal response to all repetitions, suggesting that the duration between vowel productions influences the effect of previous productions on sensorimotor cortex activity. Our findings correspond with previous studies for finger movements and bear relevance for the development of brain-computer interfaces that employ speech decoding based on brain signals, in that past utterances will need to be taken into account for these systems to work accurately.

Fingerstroke time estimates for touchscreen-based mobile gaming interaction

The growing popularity of gaming applications and ever-faster mobile carrier networks have called attention to an intriguing issue that is closely related to command input performance. A challenging mirroring game service, which simultaneously provides game service to both PC and mobile phone users, allows them to play games against each other with very different control interfaces. Thus, for efficient mobile game design, it is essential to apply a new predictive model for measuring how potential touch input compares to the PC interfaces. The present study empirically tests the keystroke-level model (KLM) for predicting the time performance of basic interaction controls on the touch-sensitive smartphone interface (i.e., tapping, pointing, dragging, and flicking). A modified KLM, tentatively called the fingerstroke-level model (FLM), is proposed using time estimates on regression models.

Dissociation between neuronal activity in sensorimotor cortex and hand movement revealed as a function of movement rate

It is often assumed that similar behavior is generated by the same brain activity. However, this does not take into account the brain state or recent behavioral history and movement initiation or continuation may not be similarly generated in the brain. To study whether similar movements are generated by the same brain activity, we measured neuronal population activity during repeated movements. Three human subjects performed a motor repetition task in which they moved their hand at four different rates (0.3, 0.5, 1, and 2 Hz). From high-resolution electrocorticography arrays implanted on motor and sensory cortex, high-frequency power (65-95 Hz) was extracted as a measure of neuronal population activity. During the two faster movement rates, high-frequency power was significantly suppressed, whereas movement parameters remained highly similar. This suppression was nonlinear: after the initial movement, neuronal population activity was reduced most strongly, and the data fit a model in which a fast decline rapidly converged to saturation. The amplitude of the beta-band suppression did not change with different rates. However, at the faster rates, beta power did not return to baseline between movements but remained suppressed. We take these findings to indicate that the extended beta suppression at the faster rates, which may suggest a release of inhibition in motor cortex, facilitates movement initiation. These results show that the relationship between behavior and neuronal activity is not consistent: recent movement influences the state of motor cortex and facilitates next movements by reducing the required level of neuronal activity.

Rhythmic movements are larger and faster but with the same frequency on removal of visual feedback

The brain controls rhythmic movement through neural circuits combining visual information with proprioceptive information from the limbs. Although rhythmic movements are fundamental to everyday activities the specific details of the responsible control mechanisms remain elusive. We tested 39 young adults who performed flexion/extension movements of the forearm. We provided them with explicit knowledge of the amplitude and the speed of their movements, whereas frequency information was only implicitly available. In a series of 3 experiments, we demonstrate a tighter control of frequency compared with amplitude or speed. We found that in the absence of visual feedback, movements had larger amplitude and higher peak speed while maintaining the same frequency as when visual feedback was available; this was the case even when participants were aware of performing overly large and fast movements. Finally, when participants were asked to modulate continuously movement frequency, but not amplitude, we found the local coefficient of variability of movement frequency to be lower than that of amplitude. We suggest that a misperception of the generated amplitude in the absence of visual feedback, coupled with a highly accurate perception of generated frequency, leads to the performance of larger and faster movements with the same frequency when visual feedback is not available. Relatively low local coefficient of variability of frequency in a task that calls for continuous change in movement frequency suggests that we tend to operate at a constant frequency at the expense of variation in amplitude and peak speed.

Separate representations of dynamics in rhythmic and discrete movements: evidence from motor learning

Rhythmic and discrete arm movements occur ubiquitously in everyday life, and there is a debate as to whether these two classes of movements arise from the same or different underlying neural mechanisms. Here we examine interference in a motor-learning paradigm to test whether rhythmic and discrete movements employ at least partially separate neural representations. Subjects were required to make circular movements of their right hand while they were exposed to a velocity-dependent force field that perturbed the circularity of the movement path. The direction of the force-field perturbation reversed at the end of each block of 20 revolutions. When subjects made only rhythmic or only discrete circular movements, interference was observed when switching between the two opposing force fields. However, when subjects alternated between blocks of rhythmic and discrete movements, such that each was uniquely associated with one of the perturbation directions, interference was significantly reduced. Only in this case did subjects learn to corepresent the two opposing perturbations, suggesting that different neural resources were employed for the two movement types. Our results provide further evidence that rhythmic and discrete movements employ at least partially separate control mechanisms in the motor system.

Modeling discrete and rhythmic movements through motor primitives: a review

Rhythmic and discrete movements are frequently considered separately in motor control, probably because different techniques are commonly used to study and model them. Yet the increasing interest in finding a comprehensive model for movement generation requires bridging the different perspectives arising from the study of those two types of movements. In this article, we consider discrete and rhythmic movements within the framework of motor primitives, i.e., of modular generation of movements. In this way we hope to gain an insight into the functional relationships between discrete and rhythmic movements and thus into a suitable representation for both of them. Within this framework we can define four possible categories of modeling for discrete and rhythmic movements depending on the required command signals and on the spinal processes involved in the generation of the movements. These categories are first discussed in terms of biological concepts such as force fields and central pattern generators and then illustrated by several mathematical models based on dynamical system theory. A discussion on the plausibility of theses models concludes the work.

Rhythmic muscular activation pattern for fast figure-eight movement

OBJECTIVE

To address the question of how the CNS generates muscle activation patterns for complex gestures, we have chosen to study a figure-eight movement. We hypothesized that the well defined rhythmic aspect of this figure will provide further insights into the temporal features of multi-muscular commands.

METHODS

Subjects performed, as fast as possible, figure-eights initiated in the center of the figure with 4 different initial directions and 2 positions of the shoulder. We extracted the temporal modulation of the EMG patterns by calculating conjugate cross-correlation functions.

RESULTS

(1) The muscular command was tuned with respect to the rotational direction of the figure-eight, (2) two sets of synergistic muscles acted in a reciprocal mode, and (3) these reciprocal commands presented an invariant temporal correlation with the spatial component of the velocity having the highest frequency.

CONCLUSION

Our results suggest that the rhythmic features of certain drawing movements favor the partitioning of the muscles into synergistic groups acting in a reciprocal mode. The inclusion of an individual muscle in one group or the other takes into account the expected number of changes of direction in the movement as a whole.

SIGNIFICANCE

Muscular temporal synergies may depend on the rhythmic features of the trajectory.

Steady-state movement related potentials for brain-computer interfacing

An approach for brain-computer interfacing (BCI) by analysis of steady-state movement related potentials (ssMRPs) produced during rhythmic finger movements is proposed in this paper. The neurological background of ssMRPs is briefly reviewed. Averaged ssMRPs represent the development of a lateralized rhythmic potential, and the energy of the EEG signals at the finger tapping frequency can be used for single-trial ssMRP classification. The proposed ssMRP-based BCI approach is tested using the classic Fisher's linear discriminant classifier. Moreover, the influence of the current source density transform on the performance of BCI system is investigated. The averaged correct classification rates (CCRs) as well as averaged information transfer rates (ITRs) for different sliding time windows are reported. Reliable single-trial classification rates of 88%-100% accuracy are achievable at relatively high ITRs. Furthermore, we have been able to achieve CCRs of up to 93% in classification of the ssMRPs recorded during imagined rhythmic finger movements. The merit of this approach is in the application of rhythmic cues for BCI, the relatively simple recording setup, and straightforward computations that make the real-time implementations plausible.

Altered Gravity Highlights Central Pattern Generator Mechanisms

In many nonprimate species, rhythmic patterns of activity such as locomotion or respiration are generated by neural networks at the spinal level. These neural networks are called central pattern generators (CPGs). Under normal gravitational conditions, the energy efficiency and the robustness of human rhythmic movements are due to the ability of CPGs to drive the system at a pace close to its resonant frequency. This property can be compared with oscillators running at resonant frequency, for which the energy is optimally exchanged with the environment. However, the ability of the CPG to adapt the frequency of rhythmic movements to new gravitational conditions has never been studied. We show here that the frequency of a rhythmic movement of the upper limb is systematically influenced by the different gravitational conditions created in parabolic flight. The period of the arm movement is shortened with increasing gravity levels. In weightlessness, however, the period is more dependent on instructions given to the participants, suggesting a decreased influence of resonant frequency. Our results are in agreement with a computational model of a CPG coupled to a simple pendulum under the control of gravity. We demonstrate that the innate modulation of rhythmic movements by CPGs is highly flexible across gravitational contexts. This further supports the involvement of CPG mechanisms in the achievement of efficient rhythmic arm movements. Our contribution is of major interest for the study of human rhythmic activities, both in a normal Earth environment and during microgravity conditions in space.

Distinct timing mechanisms produce discrete and continuous movements

The differentiation of discrete and continuous movement is one of the pillars of motor behavior classification. Discrete movements have a definite beginning and end, whereas continuous movements do not have such discriminable end points. In the past decade there has been vigorous debate whether this classification implies different control processes. This debate up until the present has been empirically based. Here, we present an unambiguous non-empirical classification based on theorems in dynamical system theory that sets discrete and continuous movements apart. Through computational simulations of representative modes of each class and topological analysis of the flow in state space, we show that distinct control mechanisms underwrite discrete and fast rhythmic movements. In particular, we demonstrate that discrete movements require a time keeper while fast rhythmic movements do not. We validate our computational findings experimentally using a behavioral paradigm in which human participants performed finger flexion-extension movements at various movement paces and under different instructions. Our results demonstrate that the human motor system employs different timing control mechanisms (presumably via differential recruitment of neural subsystems) to accomplish varying behavioral functions such as speed constraints.

A fundamental question in motor control research is whether distinct movement classes exist. Candidate classes are discrete and continuous movement. Discrete movements have a definite beginning and end, whereas continuous movements do not have such discriminable end points. In the past decade there has been vigorous, predominantly empirically based debate whether this classification implies different control processes. We present a non-empirical classification based on mathematical theorems that unambiguously sets discrete and continuous rhythmic movements apart through their topological structure in phase space. By computational simulations of representative modes of each class we show that discrete movements can only be executed repetitively at paces lower than approximately 2.0 Hz. In addition, we performed an experiment in which human participants performed finger flexion-extension movements at various movement paces and under different instructions. Through a topological analysis of the flow in state space, we show that distinct control mechanisms underwrite human discrete and fast rhythmic movements: discrete movements require a time keeper, while fast rhythmic movements do not. Our results demonstrate that the human motor system employs different timing control mechanisms (presumably via differential recruitment of neural subsystems) to accomplish varying behavioral functions such as speed constraints.

Steady-state movement related potentials for brain computer interfacing

An approach for brain computer interfacing (BCI) by analysis of the steady-state movement related potentials (ssMRP) is proposed in this paper. The neurological background of the ssMRPs which are primarily studied by means of the averaged electroencephalogram (EEG) signals are briefly reviewed. A simple feature extraction method is suggested for single trial ssMRP processing. The proposed BCI paradigm is tested by using the Fishers linear discriminant (FLD) classifier. The novelty of this approach is mainly in the application of rhythmic cues for BCI, simple recording setup, and straightforward computations which make the real-time implementations plausible.

The type of visual information mediates eye and hand movement bias when aiming to a Müller–Lyer illusion

Aiming bias typically influences perception but action towards the illusory stimulus is often unaffected. Recent studies, however, have shown that the type of information available is a predictor for the expression of action bias. In the present cyclical aiming experiment, the type of information (retinal and extra-retinal) was manipulated in order to investigate the differential contributions of different cues on both eye and hand movements. The results showed that a Müller-Lyer illusion caused very similar perturbation effects on hand and eye-movement amplitudes and this bias was mediated by the type of information available on-line. Interestingly, the impact of the illusion on goal-directed movement was smaller, when information about the figure but not the hand was provided for on-line control. Saccadic information did not influence the size of the effect of a Müller-Lyer illusion on hand movements. Furthermore, the illusions did not alter the eye-hand coordination pattern. The timing of saccade termination was strongly linked to hand movement kinematics. The present results are not consistent with current dichotomous models of perception and action or movement planning and on-line control. Rather, they suggest that the type of information available for movement planning mediates the size of the illusory effects. Overall, it has been demonstrated that movement planning and control processes are versatile operations, which have the ability to adapt to the type of information available.

The advantage of cyclic over discrete movements remains evident following changes in load and amplitude

Previous studies suggested that the advantage in speed accuracy trade-off of cyclic over discrete aiming tasks with the upper limbs may be associated with the operation of spinal neural oscillators, as in locomotion. Similar to the locomotor rhythm that is fairly robust and can accommodate changes in loading or stride length, we predicted that cyclic aiming tasks would be equally resistant to changes in load or amplitude, thereby preserving the advantage over discrete tasks. To test the hypothesis, cyclic and discrete aiming movements were performed with and without loading of the hand. Furthermore a "complex" condition was introduced in which the distance between the targets that the participants moved to alternated between 2.5 and 5 cm. In all cases, two target sizes were used to test spatial accuracy and to be able to calculate the Index of Performance (IP). Findings revealed that even though part of the advantage of the cyclic over the discrete regime was lost during the complex movement pattern and with addition of weight, the former remained superior to the latter. Furthermore, adding weight did not change the oscillation frequency in the cyclic movements. It is concluded that the superiority of cyclic movements over discrete ones is fairly robust, consistent with the high degree of flexibility that is typically observed in neural oscillators.

The Neural Basis of Human Dance

Human dance was investigated with positron emission tomography to identify its systems-level organization. Three core aspects of dance were examined: entrainment, meter and patterned movement. Amateur dancers performed small-scale, cyclically repeated tango steps on an inclined surface to the beat of tango music, without visual guidance. Entrainment of dance steps to music, compared to self-pacing of movement, was supported by anterior cerebellar vermis. Movement to a regular, metric rhythm, compared to movement to an irregular rhythm, implicated the right putamen in the voluntary control of metric motion. Spatial navigation of leg movement during dance, when controlling for muscle contraction, activated the medial superior parietal lobule, reflecting proprioceptive and somatosensory contributions to spatial cognition in dance. Finally, additional cortical, subcortical and cerebellar regions were active at the systems level. Consistent with recent work on simpler, rhythmic, motor-sensory behaviors, these data reveal the interacting network of brain areas active during spatially patterned, bipedal, rhythmic movements that are integrated in dance.