Rate control and quality assurance during rhythmic force tracking

Potential Motor Benefits of Visual Feedback of Error Reduction for Older Adults

This study investigated how visual feedback of virtual error reduction (ER) modified the visuomotor performance of older adults with limited attentional capacity. Error structures of young and older adults during birhythmic force tracking were contrasted when the visualized error size was exact or half of the actual size. As compared with full-size error feedback, ER feedback improved the force tracking symmetry of older adults, but undermined that of young adults. Extended Poincaré analysis revealed that young adults presented greater short-term error variability (mean value of κ-lagged SD1 of the error signal) with ER feedback, which led to a smaller mean value of κ-lagged SD1 of the error signal for older adults. The ER-related task improvement of the older adults was negatively correlated with the size of the tracking errors with real error feedback and positively correlated with ER-related increases in force spectral symmetry and decreases in the mean value of κ-lagged SD1 of the error signal. ER feedback could advance visuomotor tasks for older adults who perform worse with full-size visual feedback by the enhancement of self-efficacy and stabilization of negative internal feedback.

Contrasting Age Effects on Complexity of Tracking Force and Force Fluctuations During Monorhythmic Contraction

This study contrasted the stochastic force component between young and older adults, who performed pursuit tracking/compensatory tracking by exerting in-phase/antiphase forces to match a sinusoidal target. Tracking force was decomposed into the force component containing the target frequency and the nontarget force fluctuations (stochastic component). Older adults with inferior task performance had higher complexity (entropy across time; p = .005) in total force. For older adults, task errors were negatively correlated with force fluctuation complexity (pursuit tracking: r = -.527 to -.551; compensatory tracking: r = -.626 to -.750). Notwithstanding an age-related increase in total force complexity (p = .004), older adults exhibited lower complexity of the stochastic force component than young adults did (low frequency: p = .017; high frequency: p = .035). Those older adults with a higher complexity of stochastic force had better task performance due to the underlying use of a richer gradation strategy to compensate for impaired oscillatory control.

Perceptual influences of error size on voluntary force control during a compound sinusoidal force task

Visual feedback that provides error information is critical to task quality and motor adjustments. This study investigated how the size of perceived errors via visual feedback affected rate control and force gradation strategy of a designate force task. Fourteen young adults coupled force exertions to a compound sinusoidal signal (0.2 Hz and 0.5 Hz) that fluctuated around a mean level of 30% of maximal voluntary contraction, when the size of execution errors were differently scaled with the error amplification factors. In the low (LAF) and high (HAF) amplification factor conditions, the execution errors in the visual display half and double of the real errors, respectively. The visualized error was the real errors in the medium amplification factor (MAF) condition. In addition to a phase-lead of force output, the LAF condition that virtually reduced the size of error feedback associated with a poorer task accuracy than the MAF and HAF conditions. Virtual increase in error size of visual feedback selectively suppressed the fast target force at 0.5 Hz. In addition, complexity and high-frequency components (>0.75 Hz) of force outputs multiplied progressively with increasing error size. Error-enhancing feedback suppressed fast target force, accentuating the use of error information to tune force output, whereas error-reducing feedback enhanced fast target force in favor of predictive force control.

Paradigm Shifts in Voluntary Force Control and Motor Unit Behaviors with the Manipulated Size of Visual Error Perception

The detection of error information is an essential prerequisite of a feedback-based movement. This study investigated the differential behavior and neurophysiological mechanisms of a cyclic force-tracking task using error-reducing and error-enhancing feedback. The discharge patterns of a relatively large number of motor units (MUs) were assessed with custom-designed multi-channel surface electromyography following mathematical decomposition of the experimentally-measured signals. Force characteristics, force-discharge relation, and phase-locking cortical activities in the contralateral motor cortex to individual MUs were contrasted among the low (LSF), normal (NSF), and high scaling factor (HSF) conditions, in which the sizes of online execution errors were displayed with various amplification ratios. Along with a spectral shift of the force output toward a lower band, force output with a more phase-lead became less irregular, and tracking accuracy was worse in the LSF condition than in the HSF condition. The coherent discharge of high phasic (HP) MUs with the target signal was greater, and inter-spike intervals were larger, in the LSF condition than in the HSF condition. Force-tracking in the LSF condition manifested with stronger phase-locked EEG activity in the contralateral motor cortex to discharge of the (HP) MUs (LSF > NSF, HSF). The coherent discharge of the (HP) MUs during the cyclic force-tracking predominated the force-discharge relation, which increased inversely to the error scaling factor. In conclusion, the size of visualized error gates motor unit discharge, force-discharge relation, and the relative influences of the feedback and feedforward processes on force control. A smaller visualized error size favors voluntary force control using a feedforward process, in relation to a selective central modulation that enhance the coherent discharge of (HP) MUs.

Predicting hand forces from scalp electroencephalography during isometric force production and object grasping

In this study, we demonstrate the feasibility of predicting hand forces from brain activity recorded with scalp electroencephalography (EEG). Ten able-bodied subjects participated in two tasks: an isometric force production task and a grasp-and-lift task using unconstrained and constrained grasps. We found that EEG electrodes spanning central areas of the scalp were highly correlated to force rate trajectories. Moreover, EEG grand averages in central sites resembled force rate trajectories as opposed to force trajectories. The grasp-and-lift task resulted in higher decoding accuracies than the isometric force production task: across nine subjects, median accuracies for the isometric force production task were r=0.35 whereas median accuracies for unconstrained grasping were r=0.51 and for constrained grasping were r=0.50. Such results could lead to an understanding of the neural representation behind the control of hand forces and could be implemented in the neural control of closed-loop hand-based neuroprostheses.