Low-frequency Motor Cortex EEG Predicts Four Rates of Force Development.

The movement-related cortical potential (MRCP) is a low-frequency component of the electroencephalography (EEG) signal that originates from the motor cortex and surrounding cortical regions. As the MRCP reflects both the intention and execution of motor control, it has the potential to serve as a communication interface between patients and neurorehabilitation robots. In this study, we investigated the EEG signal recorded centered at the Cz electrode with the aim of decoding four rates of force development (RFD) during isometric contractions of the tibialis anterior muscle. The four levels of RFD were defined with respect to the maximum voluntary contraction (MVC) of the muscle as follows: Slow (20% MVC/s), Medium (30% MVC/s), Fast (60% MVC/s), and Ballistic (120% MVC/s). Three feature sets were assessed for describing the EEG traces in the classification process. These included: (i) MRCP Morphological Characteristics in the δ-band, such as timing and amplitude; (ii) MRCP Statistical Characteristics in the δ-band, such as standard deviation, mean, and kurtosis; and (iii) Wideband Time-frequency Features in the 0.1-90 Hz range. The four levels of RFD were accurately classified using a support vector machine. When utilizing the Wideband Time-frequency Features, the accuracy was 83% ± 9% (mean ± SD). Meanwhile, when using the MRCP Statistical Characteristics, the accuracy was 78% ± 12% (mean ± SD). The analysis of the MRCP waveform revealed that it contains highly informative data on the planning, execution, completion, and duration of the isometric dorsiflexion task. The temporal analysis emphasized the importance of the δ-band in translating to motor command, and this has promising implications for the field of neural engineering systems.

Muscle activity and hypoalgesia in blood flow restricted versus unrestricted effort-matched resistance exercise in healthy adults.

This study assessed muscle activity (root mean square, RMS, and median frequency, MDF) to evaluate the acute response to blood flow restriction (BFR) resistance exercise (RE) and conventional moderate intensity (MI) RE. We also performed exploratory analyses of differences based on sex and exercise-induced hypoalgesia (EIH). Fourteen asymptomatic individuals performed four sets of unilateral leg press with their dominant leg to volitional fatigue under two exercise conditions: BFR RE and MI RE. Dominant side rectus femoris (RF) and vastus lateralis (VL) muscle activity were measured using surface electromyography (sEMG) through exercise. RMS and MDF were calculated and compared between conditions and timepoints using a linear mixed model. Pressure pain thresholds (PPT) were tested before and immediately after exercise and used to quantify EIH. Participants were then divided into EIH responders and nonresponders, and the differences on RMS and MDF were compared between the two groups using Hedges' g. RMS significantly increased over time (RF: p = 0.0039; VL: p = 0.001) but not between conditions (RF: p = 0.4; VL: p = 0.67). MDF decreased over time (RF: p = 0.042; VL: p < 0.001) but not between conditions (RF: p = 0.74; VL: p = 0.77). Consistently lower muscle activation was found in females compared with males (BRF, RF: g = 0.63; VL, g = 0.5. MI, RF: g = 0.72; VL: g = 1.56), with more heterogeneous findings in MDF changes. For BFR, EIH responders showed greater RMS changes (Δ RMS) (RF: g = 0.90; VL: g = 1.21) but similar MDF changes (Δ MDF) (RF: g = 0.45; VL: g = 0.28) compared to nonresponders. For MI, EIH responders demonstrated greater increase on Δ RMS (g = 0.61) and decrease on Δ MDF (g = 0.68) in RF but similar changes in VL (Δ RMS: g = 0.40; Δ MDF: g = 0.39). These results indicate that when exercising to fatigue, no statistically significant difference was observed between BFR RE and conventional MI RE in Δ RMS and Δ MDF. Lower muscle activity was noticed in females. While exercising to volitional fatigue, muscle activity may contribute to EIH.

Lower-Limb Non-Parametric Functional Muscle Network: Test-Retest Reliability Analysis.

Functional muscle network analysis has attracted a great deal of interest in recent years, promising high sensitivity to changes of intermuscular synchronicity, studied mostly for healthy subjects and recently for patients living with neurological conditions (e.g., those caused by stroke). Despite the promising results, the between- and within-session reliability of the functional muscle network measures are yet to be established. Here, for the first time, we question and evaluate the test-retest reliability of non-parametric lower-limb functional muscle networks for controlled and lightly-controlled tasks, i.e., sit-to-stand, and over-the-ground walking, respectively, in healthy subjects. Fifteen subjects (eight females) were included over two sessions on two different days. The muscle activity was recorded using 14 surface electromyography (sEMG) sensors. The intraclass correlation coefficient (ICC) of the within-session and between-session trials was quantified for the various network metrics, including degree and weighted clustering coefficient. In order to compare with common classical sEMG measures, the reliabilities of the root mean square (RMS) of sEMG and the median frequency (MDF) of sEMG were also calculated. The ICC analysis revealed superior between-session reliability for muscle networks, with statistically significant differences when compared to classic measures. This paper proposed that the topographical metrics generated from functional muscle network can be reliably used for multi-session observations securing high reliability for quantifying the distribution of synergistic intermuscular synchronicities of both controlled and lightly controlled lower limb tasks. In addition, the low number of sessions required by the topographical network metrics to reach reliable measurements indicates the potential as biomarkers during rehabilitation.

Interday Reliability of Upper-limb Geometric MyoPassivity Map for Physical Human-Robot Interaction.

The value of intrinsic energetic behavior of human biomechanics has recently been recognized and exploited in physical human-robot interaction (pHRI). The authors have recently proposed the concept of "Biomechanical Excess of Passivity," based on nonlinear control theory, to construct a user-specific energetic map. The map would assess the behavior of the upper-limb in absorbing the kinesthetic energy when interacting with robots. Integrating such knowledge into the design of pHRI stabilizers can reduce the conservatism of the control by unleashing hidden energy reservoirs indicating a less conservative margin of stability. The outcome would enhance the system's performance, such as rendering kinesthetic transparency of (tele)haptics systems. However, current methods require an offline data-driven identification procedure prior to each operation to estimate the energetic map of human biomechanics. This can be time-consuming and challenge users susceptible to fatigue. In this study, for the first time, we investigate the interday reliability of upper-limb passivity maps in a sample of five healthy subjects. Our statistical analyses indicate that the identified passivity map is highly reliable in estimating the expected energetic behavior based on Intraclass correlation coefficient analysis (conducted on different days and with various interactions). The results illustrate that a one-shot estimate is a reliable measure to be used repeatedly in biomechanics-aware pHRI stabilization, enhancing practicality in real-life scenarios.

Non-parametric Functional Muscle Network as a Robust Biomarker of Fatigue.

Characterization of fatigue using surface electromyography (sEMG) data has been motivated for rehabilitation and injury-preventative technologies. Current sEMG-based models of fatigue are limited due to (a) linear and parametric assumptions, (b) lack of a holistic neurophysiological view, and (c) complex and heterogeneous responses. This paper proposes and validates a data-driven non-parametric functional muscle network analysis to reliably characterize fatigue-related changes in synergistic muscle coordination and distribution of neural drive at the peripheral level. The proposed approach was tested on data collected in this study from the lower extremities of 26 asymptomatic volunteers (13 subjects were assigned to the fatigue intervention group, and 13 age/gender-matched subjects were assigned to the control group). Volitional fatigue was induced in the intervention group by moderate-intensity unilateral leg press exercises. The proposed non-parametric functional muscle network demonstrated a consistent decrease in connectivity after the fatigue intervention, as indicated by network degree, weighted clustering coefficient (WCC), and global efficiency. The graph metrics displayed consistent and significant decreases at the group level, individual subject level, and individual muscle level. For the first time, this paper proposed a non-parametric functional muscle network and highlighted the corresponding potential as a sensitive biomarker of fatigue with superior performance to conventional spectrotemporal measures.

Linear versus Nonlinear Muscle Networks: A Case Study to Decode Hidden Synergistic Patterns During Dynamic Lower-limb Tasks.

This paper, for the first time, compares the behaviors of nonlinear versus linear muscle networks in decoding hidden peripheral synergistic neural patterns during dynamic functional tasks. In this paper, we report a case study during which one healthy subject conducts a series of four lower limb repetitive tasks. Specifically, the paper focuses on tasks that involve the right knee joint, including walking, sit-tostand, stepping, and drop-jump. Twelve muscles were recorded using the Delsys Trigno system. The linear muscle network was generated using coherence analysis, and the nonlinear network was generated using Spearman's correlation. The results show that the degree, clustering coefficient, and global efficiency of the muscle network have the highest value among tasks in the linear domain for the walking task, while a low linear synergistic network behavior for the sit-to-stand is observed. On the other hand, the results show that the nonlinear functional muscle network decodes high connectivity (degree) and clustering coefficient and efficiency for the sit-tostand when compared with other tasks. We have also developed a two-dimensional functional connectivity plane composed of linear and nonlinear features and shown that it can span the lower-limb dynamic task space. The results of this paper for the first time highlight the importance of observing both linear and nonlinear connectivity patterns, especially for complex dynamic tasks. It should also be noted that through a simultaneous EEG recording (using BrainVision System), we have shown that, indeed, cortical activity may indirectly explain highly-connected nonlinear muscle network for the sit-to-stand task, highlighting the importance of nonlinear muscle network as a neurophysiological window of observation beyond the periphery.

Nonlinear functional muscle network based on information theory tracks sensorimotor integration post stroke.

Sensory information is critical for motor coordination. However, understanding sensorimotor integration is complicated, especially in individuals with impairment due to injury to the central nervous system. This research presents a novel functional biomarker, based on a nonlinear network graph of muscle connectivity, called InfoMuNet, to quantify the role of sensory information on motor performance. Thirty-two individuals with post-stroke hemiparesis performed a grasp-and-lift task, while their muscle activity from 8 muscles in each arm was measured using surface electromyography. Subjects performed the task with their affected hand before and after sensory exposure to the task performed with the less-affected hand. For the first time, this work shows that InfoMuNet robustly quantifies changes in functional muscle connectivity in the affected hand after exposure to sensory information from the less-affected side. > 90% of the subjects conformed with the improvement resulting from this sensory exposure. InfoMuNet also shows high sensitivity to tactile, kinesthetic, and visual input alterations at the subject level, highlighting its potential use in precision rehabilitation interventions.

Perilaryngeal-Cranial Functional Muscle Network Differentiates Vocal Tasks: A Multi-Channel sEMG Approach.

OBJECTIVE: Objective evaluation of physiological responses using non-invasive methods for the assessment of vocal performance and voice disorders has attracted great interest. This paper, for the first time, aims to implement and evaluate perilaryngeal-cranial functional muscle networks. The study investigates the variations in topographical characteristics of the network and the corresponding ability to differentiate vocal tasks. METHOD: Twelve surface electromyography (sEMG) signals were collected bilaterally from six perilaryngeal and cranial muscles. Data were collected from eight subjects (four females) without a known history of voice disorders. The proposed muscle network is composed of pairwise coherence between sEMG recordings. The network metrics include (a) network degree and (b) weighted clustering coefficient (WCC). RESULTS: The varied phonation tasks showed the median degree, and WCC of the muscle network ascend monotonically, with a high effect size ( |rrb| âˆ¼ 0.5). Pitch glide, singing, and speech tasks were significantly distinguishable using degree and WCC ( |rrb| âˆ¼ 0.8). Also, pitch glide had the highest degree and WCC among all tasks (degree , WCC ). In comparison, classic spectrotemporal measures showed far less effectiveness (max |rrb|=0.12) in differentiating the vocal tasks. CONCLUSION: Perilaryngeal-cranial functional muscle network was proposed in this paper. The study showed that the functional muscle network could robustly differentiate the vocal tasks while the classic assessment of muscle activation fails to differentiate. SIGNIFICANCE: For the first time, we demonstrate the power of a perilaryngeal-cranial muscle network as a neurophysiological window to vocal performance. In addition, the study also discovers tasks with the highest network involvement, which may be utilized in the future to monitor voice disorders and rehabilitation.