Control of myoelectric prostheses and brain–machine interfaces requires learning abstract neuromotor transformations. To investigate the mechanisms underlying this ability, we trained subjects to move a two-dimensional cursor using a myoelectric-controlled interface. With the upper limb immobilized, an electromyogram from multiple hand and arm muscles moved the cursor in directions that were either intuitive or nonintuitive and with high or low variability. We found that subjects could learn even nonintuitive arrangements to a high level of performance. Muscle-tuning functions were cosine shaped and modulated so as to reduce cursor variability. Subjects exhibited an additional preference for using hand muscles over arm muscles, which resulted from a greater capacity of these to form novel, task-specific synergies. In a second experiment, nonvisual feedback from the hand was degraded with amplitude- and frequency-modulated vibration. Although vibration impaired task performance, it did not affect the rate at which learning occurred. We therefore conclude that the motor system can acquire internal models of novel, abstract neuromotor mappings even in the absence of overt movements or accurate proprioceptive signals, but that the distal motor system may be better suited to provide flexible control signals for neuromotor prostheses than structures related to the arm.
We examined whether challenging upright stance influences the execution of a grasping task. Participants reached to grasp a small sphere while standing either on a stable surface or on foam. Before reaching for the sphere, participants exhibited more body sway and greater fluctuations in the centre of pressure when standing on foam. While reaching for the sphere, the overall body posture changed less when standing on foam than when standing on the stable surface. The digits' and wrist's movements towards the sphere were no different when standing on foam than when standing on the stable surface. Presumably, the redundancy in the way movements can be performed is exploited to choose the most suitable changes in joint angles to achieve the desired movements of the digits under the prevailing conditions.
Achilles tendon vibration (ATV) alters proprioceptive input of the triceps surae muscles resulting in a posterior postural shift during standing. When this is applied in combination with a more dynamic proprioceptive perturbation, postural responses to ATV are attenuated. In this study, we applied ATV during self-paced, visually and auditory guided voluntary periodic sway in order to examine how the vibration-induced afferent input is processed and reweighted at the presence of inter-sensory guidance stimuli. Seventeen healthy adults (aged 26.7 ± 4.23 years) performed 15 cycles of periodic sway under three sensory guidance conditions: (a) self-paced, (b) auditory paced (0.25 Hz), and (c) visually driven by matching the resultant force vector to a target sine-wave (0.25 Hz). Bilateral ATV (80 Hz, 3 mm) was applied between the 5th and 10th sway cycles. ATV evoked an earlier burst onset and increased activity of the plantarflexors consistent with a reduction in the amplitude and duration of forward sway. This in turn resulted in an increase in dorsiflexors' activity in order to compensate for the greater backward sway. Postural responses to ATV were augmented when sway was auditory and visually guided. Forward sway variability increased with ATV and remained high while backward sway variability decreased in the post-vibration phase. Our results suggest that sensory context-dependent constraints that determine the degree of active control of posture and associated postural challenge involved in a particular task determine how the vibration-induced Ia afferent input will be registered and further processed by the central nervous system.
Human brainstem auditory evoked responses (BAERs) are sensory evoked potentials that can be recorded within a few milliseconds following a transient acoustic stimulus (click signal). This paper suggests a novel technique to clearly demarcate normals and patients with complaints of vertigo and deafness by computing hitherto unused power spectral parameters from the BAER signals recorded on them. The BAER spectrum of normal subjects contains three main frequency components, i.e. low-, mid- and high-frequency components around 100, 500 and 1000 Hz, respectively, which is not so in the case of diseased subjects. The spectral parameters, i.e. the mean power frequency, median frequency, the ratios of the integrated power at dominant frequencies to that of the total power in spectrum and change in spectral power (CP) between these dominant frequency components are used to classify the recorded BAER signals into those of normals and the patients, and aid the clinician in quick and better diagnosis. The ranges of CP are estimated for the different groups and appear to be the most dominant parameter in the classification of the BAER signals.
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