Weakened CMC in aging may be a major factor contributing to age-related muscle weakness, and the linear relationship between the CMC and voluntary muscle force suggests dependence of force output on translation of the descending command to muscle electrical signal.

The goal of this paper is to demonstrate a novel approach that combines Empirical Mode Decomposition (EMD) with Notch filtering to remove the electrical stimulation (ES) artifact from surface electromyogram (EMG) data for interpretation of muscle responses during functional electrical stimulation (FES) experiments. FES was applied to the rectus femoris (RF) muscle unilaterally of six able bodied (AB) and one individual with spinal cord injury (SCI). Each trial consisted of three repetitions of ES. We hypothesized that the EMD algorithm provides a suitable platform for decomposing the EMG signal into physically meaningful intrinsic mode functions (IMFs) which can be further used to isolate electrical stimulation (ES) artifact. A basic EMD algorithm was used to decompose the EMG signals collected during FES into IMFs for each repetition separately. IMFs most contaminated by ES were identified based on the standard deviation (SD) of each IMF. Each artifact IMF was Notch filtered to filter ES harmonics and added to remaining IMFs containing pure EMG data to get a version of a filtered EMG signal. Of all such versions of filtered signals generated from each artifact IMF, the one with maximum signal to noise ratio (SNR) was chosen as the final output. The validity of the filtered signal was assessed by quantitative metrics, 1) root mean squared error (RMSE) and signal to noise (SNR) ratio values obtained by comparing a clean EMG and EMD-Notch filtered signal from the combination of simulated ES and clean EMG and, 2) using EMG-force correlation analysis on the data collected from AB individuals. Finally, the potential applicability of this algorithm on a neurologically impaired population was shown by applying the algorithm on EMG data collected from an individual with SCI. EMD combined with Notch filtering successfully extracted the EMG signal buried under ES artifact. Filtering performance was validated by smaller RMSE values and greater SNR post filtering. The amplitude values of the filtered EMG signal were seen to be consistent for three repetitions of ES and there was no significant difference among the repetition for all subjects. For the individual with a SCI the algorithm was shown to successfully isolate the underlying bursts of muscle activations during FES. The data driven nature of EMD algorithm and its ability to act as a filter bank at different bandwidths make this method extremely suitable for dissecting ES induced EMG into IMFs. Such IMFs clearly show the presence of ES artifact at different intensities as well as pure artifact free EMG. This allows the application of Notch filters to IMFs containing ES artifact to further isolate the EMG. As a result of such stepwise approach, the extraction of EMG is achieved with minimal data loss. This study provides a unique approach to dissect and interpret the EMG signal during FES applications.

Muscle weakness associated with aging implicates central neural degeneration. However, role of the primary motor cortex (M1) is poorly understood, despite evidence that gains in strength in younger adults are associated with its adaptations. We investigated whether weakness of biceps brachii in aging analogously relates to processes in M1. We enrolled 20 young (22.6 Ϯ 0.87 yr) and 28 old (74.79 Ϯ 1.37 yr) right-handed participants. Using transcranial magnetic stimulation, representation of biceps in M1 was identified. We examined the effect of age and sex on strength of left elbow flexion, voluntary activation of biceps, corticospinal excitability and output, and short-interval intracortical and interhemispheric inhibition. Interhemispheric inhibition was significantly exaggerated in the old (P ϭ 0.047), while strength tended to be lower (P ϭ 0.075). Overall, women were weaker (P Ͻ 0.001). Processes of M1 related to strength or voluntary activation of biceps, but only in older adults. Corticospinal excitability was lower in weaker individuals (r ϭ 0.38), and corticospinal output, intracortical inhibition and interhemispheric inhibition were reduced too in individuals who poorly activated biceps (r ϭ 0.43, 0.54 and 0.38). Lower intracortical inhibition may reflect compensation for reduced corticospinal excitability, allowing weaker older adults to spread activity in M1 to recruit synergists and attempt to sustain motor output. Exaggerated interhemispheric inhibition, however, conflicts with previous evidence, potentially related to greater callosal damage in our older sample, our choice of proximal vs. distal muscle and differing influence of measurement of inhibition in rest vs. active states of muscle. Overall, age-specific relation of M1 to strength and muscle activation emphasizes that its adaptations only emerge when necessitated, as in a weakening neuromuscular system in aging.