Effects of supraspinal feedback on human gait: rhythmic auditory distortion.

BACKGROUND: Different types of sound cues have been used to adapt the human gait rhythm. We investigated whether young healthy volunteers followed subliminal metronome rhythm changes during gait. METHODS: Twenty-two healthy adults walked at constant speed on a treadmill following a metronome sound cue (period 566 msec). The metronome rhythm was then either increased or decreased, without informing the subjects, at 1 msec increments or decrements to reach, respectively, a low (596 msec) or a high frequency (536 msec) plateaus. After 30 steps at one of these isochronous conditions, the rhythm returned to the original period with decrements or increments of 1 msec. Motion data were recorded with an optical measurement system to determine footfall. The relative phase between sound cue (stimulus) and foot contact (response) were compared. RESULTS: Gait was entrained to the rhythmic auditory stimulus and subjects subconsciously adapted the step time and length to maintain treadmill speed, while following the rhythm changes. In most cases there was a lead error: the foot contact occurred before the sound cue. The mean error or the absolute mean relative phase increased during the isochronous high (536 msec) or low frequencies (596 msec). CONCLUSION: These results showed that the gait period is strongly "entrained" with the first metronome rhythm while subjects still followed metronome changes with larger error. This suggests two processes: one slow-adapting, supraspinal oscillator with persistence that predicts the foot contact to occur ahead of the stimulus, and a second fast process linked to sensory inputs that adapts to the mismatch between peripheral sensory input (foot contact) and supraspinal sensory input (auditory rhythm).

Compression of high-density EMG signals for trapezius and gastrocnemius muscles.

BACKGROUND: New technologies for data transmission and multi-electrode arrays increased the demand for compressing high-density electromyography (HD EMG) signals. This article aims the compression of HD EMG signals recorded by two-dimensional electrode matrices at different muscle-contraction forces. It also shows methodological aspects of compressing HD EMG signals for non-pinnate (upper trapezius) and pinnate (medial gastrocnemius) muscles, using image compression techniques. METHODS: HD EMG signals were placed in image rows, according to two distinct electrode orders: parallel and perpendicular to the muscle longitudinal axis. For the lossless case, the images obtained from single-differential signals as well as their differences in time were compressed. For the lossy algorithm, the images associated to the recorded monopolar or single-differential signals were compressed for different compression levels. RESULTS: Lossless compression provided up to 59.3% file-size reduction (FSR), with lower contraction forces associated to higher FSR. For lossy compression, a 90.8% reduction on the file size was attained, while keeping the signal-to-noise ratio (SNR) at 21.19 dB. For a similar FSR, higher contraction forces corresponded to higher SNR CONCLUSIONS: The computation of signal differences in time improves the performance of lossless compression while the selection of signals in the transversal order improves the lossy compression of HD EMG, for both pinnate and non-pinnate muscles.

Optimal autoregressive orders for myopathic electromyograms.

This paper aims to describe the optimal autoregressive order of varying-length electromyograms for myopathic subjects. Epochs of electromyography signals are modeled as outputs of autoregressive systems, for orders varying from 1 to 100. The optimal order to represent each epoch is chosen by the minimum description length criterion. Probability density functions are fitted to the histograms of the optimal orders. The lognormal function provides the best fitting, and its mean value varies linearly with the epoch length.

Epoch length and autoregressive-order selection for electromyography signals.

This study shows how different EMG-epoch lengths affect the selection of the autoregressive-model orders. Electromyography signals were divided in 25ms, 50ms, 100ms, 250ms and 500ms epochs. Order-selection criteria were applied to the least-square errors of autoregressive models. The Bayesian Information Criterion and the Minimum Description Length indicated that needle-EMG signals recorded from normal subjects at 25kHz could be represented by autoregressive models using orders below 25 for 500ms epochs, and that smaller orders could be used to represent shorter epochs.

Influence of evoked response latency and amplitude on stimulus artifact removal.

The removal of stimulus artifacts is a challenge not only to evoked potentials but also to other electrically-elicited signals such as M wave and direct cortical responses. Several techniques try to remove stimulus artifacts. However, the influence of the amplitude ratio between signal and artifact has not been reported yet. Neither has been the effect of the delay between stimulus and signal. So, this work aimed to study the relationship between the evoked response's parameters (amplitude; latency) and the associated errors, for several amplitude ratios and delays between stimulus artifact and evoked response simulation models. In order to do so, the evoked responses were represented by a mathematical function with varying amplitudes and latencies. The stimulus artifacts were represented by the concatenation of a rectangular bipolar pulse and three exponential functions. Reasonable results were obtained for these models, whenever the amplitude ratio between evoked response and artifact was between 5% and 40%, and the evoked response's latency was within the range of 3ms to 8ms. These results showed that the technique has the potential to contribute to stimulus artifact removal on somatosensory evoked potentials recorded at the elbow, for both wrist and hand stimulation of the median nerve.

Estimaçäo de parâmetros musculares via memórias associativas / Estimnation of the muscles parameters way associative memory

Estudos computacionais mostram que a elasticidade e a viscosidade de um modelo de músculo podem ser estimadas adequadamente por meio de memórias associativas. Os resultados obtidos indicam a possibilidade de uso de memórias associativas na estimação de parâmetros envolvendo dados biológicos.