Locomotion can be investigated by factorization of electromyographic (EMG) signals, e.g. with non-negative matrix factorization (NMF). This approach is a convenient concise representation of muscle activities as distributed in motor modules, activated in specific gait phases. For applying NMF, the EMG signals are analysed either as single trials, or as averaged EMG, or as concatenated EMG (data structure). The aim of this study is to investigate the influence of the data structure on the extracted motor modules. Twelve healthy men walked at their preferred speed on a treadmill while surface EMG signals were recorded for 60 s from 10 lower limb muscles. Motor modules representing relative weightings of synergistic muscle activations were extracted by NMF from 40 step cycles separately (EMGSNG), from averaging 2, 3, 5, 10, 20 and 40 consecutive cycles (EMGAVR), and from the concatenation of the same sets of consecutive cycles (EMGCNC). Five motor modules were sufficient to reconstruct the original EMG datasets (reconstruction quality > 90%), regardless of the type of data structure used. However, EMGCNC was associated with a slightly reduced reconstruction quality with respect to EMGAVR. Most motor modules were similar when extracted from different data structures (similarity > 0.85). However, the quality of the reconstructed 40-step EMGCNC datasets when using the muscle weightings from EMGAVR was low (reconstruction quality ~ 40%). On the other hand, the use of weightings from EMGCNC for reconstructing this long period of locomotion provided higher quality, especially using 20 concatenated steps (reconstruction quality ~ 80%). Although EMGSNG and EMGAVR showed a higher reconstruction quality for short signal intervals, these data structures did not account for step-to-step variability. The results of this study provide practical guidelines on the methodological aspects of synergistic muscle activation extraction from EMG during locomotion.

Objective. Electroencephalography (EEG) can assess brain activity during whole-body motion in humans but head motion can induce artifacts that obfuscate electrocortical signals. Definitive solutions for removing motion artifact from EEG have yet to be found, so creating methods to assess signal processing routines for removing motion artifact are needed. We present a novel method for investigating the influence of head motion on EEG recordings as well as for assessing the efficacy of signal processing approaches intended to remove motion artifact. Approach. We used a phantom head device to mimic electrical properties of the human head with three controlled dipolar sources of electrical activity embedded in the phantom. We induced sinusoidal vertical motions on the phantom head using a custom-built platform and recorded EEG signals with three different acquisition systems while the head was both stationary and in varied motion conditions. Main results . Recordings showed up to 80% reductions in signal-to-noise ratio (SNR) and up to 3600% increases in the power spectrum as a function of motion amplitude and frequency. Independent component analysis (ICA) successfully isolated the three dipolar sources across all conditions and systems. There was a high correlation ( r > 0.85) and marginal increase in the independent components’ (ICs) power spectrum (∼15%) when comparing stationary and motion parameters. The SNR of the IC activation was 400%–700% higher in comparison to the channel data SNR, attenuating the effects of motion on SNR. Significance. Our results suggest that the phantom head and motion platform can be used to assess motion artifact removal algorithms and compare different EEG systems for motion artifact sensitivity. In addition, ICA is effective in isolating target electrocortical events and marginally improving SNR in relation to stationary recordings.

Recent advances in electroencephalographic (EEG) acquisition allow for recordings using wet and dry sensors during whole-body motion. The large variety of commercially available EEG systems contrasts with the lack of established methods for objectively describing their performance during whole-body motion. Therefore, the aim of this study was to introduce methods for benchmarking the suitability of new EEG technologies for that context. Subjects performed an auditory oddball task using three different EEG systems (Biosemi wet – BSM, Cognionics Wet – Cwet, Conionics Dry – Cdry). Nine subjects performed the oddball task while seated and walking on a treadmill. We calculated EEG epoch rejection rate, pre-stimulus noise, signal-to-noise ratio and EEG amplitude variance across the P300 event window (CVERP) from a subset of 12 channels common to all systems. We also calculated test-retest reliability and the subject’s level of comfort while using each system. Our results showed that using the traditional 75 µV rejection threshold BSM and Cwet epoch rejection rates are ~25% and ~47% in the seated and walking conditions respectively. However, this threshold rejects ~63% of epochs for Cdry in the seated condition and excludes 100% of epochs for the majority of subjects during walking. BSM showed predominantly no statistical differences between seated and walking condition for all metrics, whereas Cwet showed increases in pre-stimulus noise and CVERP, as well as reduced signal-to-noise ratio in the walking condition. Data quality from Cdry in seated conditions were predominantly inferior in comparison to the wet systems. Test-retest reliability was mostly moderate/good for these variables, especially in seated conditions. In addition, subjects felt less discomfort and were motivated for longer recording periods while using wet EEG systems in comparison to the dry system. The proposed method was successful in identifying differences across systems that are mostly caused by motion-related artifacts and usability issues. We conclude that the extraction of the selected metrics from an auditory oddball paradigm may be used as a benchmark method for testing the performance of different EEG systems in mobile conditions. Moreover dry EEG systems may need substantial improvements to meet the quality standards of wet electrodes.

The control of movement velocity is accomplished by the modulation of the neural drive to muscle and its variation over time. In this study, we tracked motor units decomposed from HD-sEMG across shortening and lengthening contractions at increasing velocities in two submaximal load levels. We demonstrate that concentric and eccentric contractions of the tibialis anterior muscle at slow velocities are achieved by specific motor unit rate coding strategies rather than distinct recruitment schemes.

Low reproducibility and non-optimal sample sizes are current concerns in scientific research, especially within human movement studies. Therefore, this study aimed to examine the implications of different sample sizes and number of steps on data variability and statistical outcomes from kinematic and kinetics running biomechanical variables. Forty-four participants ran overground using their preferred technique (normal) and minimizing the contact sound volume (silent). Running speed, peak vertical, braking forces, and vertical average loading rate were extracted from > 40 steps/runner. Data stability was computed using a sequential estimation technique. Statistical outcomes (p values and effect sizes) from the comparison normal vs silent running were extracted from 100,000 random samples, using various combinations of sample size (from 10 to 40 runners) and number of steps (from 5 to 40 steps). The results showed that only 35% of the study sample could reach average stability using up to 10 steps across all biomechanical variables. The loading rate was consistently significantly lower during silent running compared to normal running, with large effect sizes across all combinations. However, variables presenting small or medium effect sizes (running speed and peak braking force), required > 20 runners to reach significant differences. Therefore, varying sample sizes and number of steps are shown to influence the normal vs silent running statistical outcomes in a variable-dependent manner. Based on our results, we recommend that studies involving analysis of traditional running biomechanical variables use a minimum of 25 participants and 25 steps from each participant to provide appropriate data stability and statistical power.

Balance recovery during walking requires complex sensory-motor integration. Mechanisms to avoid falls are active concomitantly with human locomotion motor patterns. It has been suggested that gait can be described by a set of motor modules (synergies), but little is known on the modularity of gait during recovery of balance due to unexpected slips. Our hypothesis was that muscular activation during reactive recovery of balance during gait has a modular organization. The aim of the study was to verify this hypothesis when perturbations were delivered in different directions. Eight healthy men walked on a 7-m walkway, which had a moveable force platform embedded in the middle. Subjects experienced unperturbed walking as well as perturbations delivered in the sagittal (forward and backward) and frontal (leftward and rightward) planes. Bilateral full-body kinematics and surface electromyography (EMG) from lower limbs, trunk, and neck were recorded during walking. Synergies and activation signals were extracted from surface EMG signals. Four modules were sufficient to explain the unperturbed gait and the gait perturbed in any of the perturbation directions. Moreover, three of four modules extracted from the unperturbed gait were the same for gait perturbed forward, leftward, and rightward (similarity in synergies = 0.94 ± 0.03). On the other hand, the activation signals were different between unperturbed and perturbed gait (average correlation coefficient = 0.55 ± 0.16). These strategies to recover balance were robust across subjects. In conclusion, changes in lower limb and trunk kinematics provoked by perturbations were reflected in minimal adjustments in the muscular modular organization of walking, with three of four modules preserved from normal walking. Conversely, the activation signals were all substantially influenced by the perturbations, being the result of integration of afferent information and supraspinal control.