Electroencephalography (EEG) and electromyography (EMG) are widespread and well-known quantitative techniques used for gathering biological signals at cortical and muscular levels, respectively. Indeed, they provide relevant insights for increasing knowledge in different domains, such as physical and cognitive, and research fields, including neuromotor rehabilitation. So far, EEG and EMG techniques have been independently exploited to guide or assess the outcome of the rehabilitation, preferring one technique over the other according to the aim of the investigation. More recently, the combination of EEG and EMG started to be considered as a potential breakthrough approach to improve rehabilitation effectiveness. However, since it is a relatively recent research field, we observed that no comprehensive reviews available nor standard procedures and setups for simultaneous acquisitions and processing have been identified. Consequently, this paper presents a systematic review of EEG and EMG applications specifically aimed at evaluating and assessing neuromotor performance, focusing on cortico-muscular interactions in the rehabilitation field. A total of 213 articles were identified from scientific databases, and, following rigorous scrutiny, 55 were analyzed in detail in this review. Most of the applications are focused on the study of stroke patients, and the rehabilitation target is usually on the upper or lower limbs. Regarding the methodological approaches used to acquire and process data, our results show that a simultaneous EEG and EMG acquisition is quite common in the field, but it is mostly performed with EMG as a support technique for more specific EEG approaches. Non-specific processing methods such as EEG-EMG coherence are used to provide combined EEG/EMG signal analysis, but rarely both signals are analyzed using state-of-the-art techniques that are gold-standard in each of the two domains. Future directions may be oriented toward multi-domain approaches able to exploit the full potential of combined EEG and EMG, for example targeting a wider range of pathologies and implementing more structured clinical trials to confirm the results of the current pilot studies.

Muscle synergy analysis investigates the neurophysiological mechanisms that the central nervous system employs to coordinate muscles. Several models have been developed to decompose electromyographic (EMG) signals into spatial and temporal synergies. However, using multiple approaches can complicate the interpretation of results. Spatial synergies represent invariant muscle weights modulated with variant temporal coefficients; temporal synergies are invariant temporal profiles that coordinate variant muscle weights. While non-negative matrix factorization allows to extract both spatial and temporal synergies, the comparison between the two approaches was rarely investigated targeting a large set of multi-joint upper-limb movements. Spatial and temporal synergies were extracted from two datasets with proximal (16 subjects, 10M, 6F) and distal upper-limb movements (30 subjects, 21M, 9F), focusing on their differences in reconstruction accuracy and inter-individual variability. We showed the existence of both spatial and temporal structure in the EMG data, comparing synergies with those from a surrogate dataset in which the phases were shuffled preserving the frequency content of the original data. The two models provide a compact characterization of motor coordination at the spatial or temporal level, respectively. However, a lower number of temporal synergies are needed to achieve the same reconstruction R²: spatial and temporal synergies may capture different hierarchical levels of motor control and are dual approaches to the characterization of low-dimensional coordination of the upper-limb. Last, a detailed characterization of the structure of the temporal synergies suggested that they can be related to intermittent control of the movement, allowing high flexibility and dexterity. These results improve neurophysiology understanding in several fields such as motor control, rehabilitation, and prosthetics.

The muscle synergy approach is used to evaluate motor control and to quantitatively determine the number and structure of the modules underlying movement. In experimental studies regarding the upper limb, typically 8 to 16 EMG probes are used depending on the application, although the number of muscles involved in motor generation is higher. Therefore, the number of motor modules may be underestimated and the structure altered with the standard spatial synergy model based on the non-negative matrix factorization (NMF). In this study, we compared the number and structure of muscle synergies when considering 12 muscles (an “average” condition that represents previous studies) and 32 muscles of the upper limb, also including multiple muscle heads and deep muscles. First, we estimated the muscle activations with an upper-limb model in OpenSim using data from multi-directional reaching movements acquired in experimental sessions; then, spatial synergies were extracted from EMG activations from 12 muscles and from 32 muscles and their structures were compared. Finally, we compared muscle synergies obtained from OpenSim and from real experimental EMG signals to assess the reliability of the results. Interestingly, we found that on average, an additional synergy is needed to reconstruct the same R² level with 32 muscles with respect to 12 muscles; synergies have a very similar structure, although muscles with comparable physiological functions were added to the synergies extracted with 12 muscles. The additional synergies, instead, captured patterns that could not be identified with only 12 muscles. We concluded that current studies may slightly underestimate the number of controlled synergies, even though the main structure of synergies is not modified when adding more muscles. We also show that EMG activations estimated with OpenSim are in partial (but not complete) agreement with experimental recordings. These findings may have significative implications for motor control and clinical studies.

Recent human-centered developments in the industrial field (Industry 5.0) lead companies and stakeholders to ensure the wellbeing of their workers with assessments of upper limb performance in the workplace, with the aim of reducing work-related diseases and improving awareness of the physical status of workers, by assessing motor performance, fatigue, strain and effort. Such approaches are usually developed in laboratories and only at times they are translated to on-field applications; few studies summarized common practices for the assessments. Therefore, our aim is to review the current state-of-the-art approaches used for the assessment of fatigue, strain and effort in working scenarios and to analyze in detail the differences between studies that take place in the laboratory and in the workplace, in order to give insights on future trends and directions. A systematic review of the studies aimed at evaluating the motor performance, fatigue, strain and effort of the upper limb targeting working scenarios is presented. A total of 1375 articles were found in scientific databases and 288 were analyzed. About half of the scientific articles are focused on laboratory pilot studies investigating effort and fatigue in laboratories, while the other half are set in working places. Our results showed that assessing upper limb biomechanics is quite common in the field, but it is mostly performed with instrumental assessments in laboratory studies, while questionnaires and scales are preferred in working places. Future directions may be oriented towards multi-domain approaches able to exploit the potential of combined analyses, exploitation of instrumental approaches in workplace, targeting a wider range of people and implementing more structured trials to translate pilot studies to real practice.

A new direction in the study of motor control was opened about two decades ago with the introduction of a model for the generation of motor commands as combination of muscle synergies. Muscle synergies provide a simple yet quantitative framework for analyzing the hierarchical and modular architecture of the human motor system. However, to gain insights on the functional role of muscle synergies, they should be related to the task space. The recently introduced mixed-matrix factorization (MMF) algorithm extends the standard approach for synergy extraction based on non-negative matrix factorization (NMF) allowing to factorize data constituted by a mixture of non-negative variables (e.g. EMGs) and unconstrained variables (e.g. kinematics, naturally including both positive and negative values). The kinematic-muscular synergies identified by MMF provide a direct link between muscle synergies and the task space. In this contribution, we support the adoption of MMF through a Matlab toolbox for the extraction of kinematic-muscular synergies and a set of practical guidelines to allow biomedical researchers and clinicians to exploit the potential of this novel approach. MMF is implemented in the SynergyAnalyzer toolbox using an object-oriented approach. In addition to the MMF algorithm, the toolbox includes standard methods for synergy extraction (NMF and PCA), as well as methods for pre-processing EMG and kinematic data, and for plotting data and synergies. As an example of MMF application, kinematic-muscular synergies were extracted from EMG and kinematic data collected during reaching movements towards 8 targets on the sagittal plane. Instructions and command lines to achieve such results are illustrated in detail. The toolbox has been released as an open-source software on GitHub under the GNU General Public License. Thanks to its ease of use and adaptability to a variety of datasets, SynergyAnalyzer will facilitate the adoption of MMF to extract kinematic-muscular synergies from mixed EMG and kinematic data, a useful approach in biomedical research to better understand and characterize the functional role of muscle synergies.

In the last two decades, muscle synergies analysis has been commonly used to assess the neurophysiological mechanisms underlying human motor control. Several synergy models and algorithms have been employed for processing the electromyographic (EMG) signal, and it has been shown that the coordination of motor control is characterized by the presence of phasic (movement-related) and tonic (anti-gravity and related to co-contraction) EMG components. Neural substrates indicate that phasic and tonic components have non-homogeneous origin; however, it is still unclear if these components are generated by the same set of synergies or by distinct synergies. This study aims at testing whether phasic and tonic components are generated by distinct phasic and tonic synergies or by the same set of synergies with phasic and tonic activation coefficients. The study also aims at characterizing the differences between the phasic and the tonic synergies. Using a comprehensive mapping of upper-limb point-to-point movements, synergies were extracted from phasic and tonic EMG signal separately, estimating the tonic components with a linear ramp model. The goodness of reconstruction (R²) as a function of the number of synergies was compared, and sets of synergies extracted from each dataset at three R² threshold levels (0.80, 0.85, 0.90) were retained for further analysis. Then, shared, phasic-specific, and tonic-specific synergies were extracted from the two datasets concatenated. The dimensionality of the synergies shared between the phasic and the tonic datasets was estimated with a bootstrap procedure based on the evaluation of the distribution of principal angles between the subspaces spanned by phasic and tonic synergies due to noise. We found only few shared synergies, indicating that phasic and tonic synergies have in general different structures. To compare consistent differences in synergy composition, shared, phasic-specific, and tonic-specific synergies were clustered separately. Phasic-specific clusters were more numerous than tonic-specific ones, suggesting that they were more differentiated among subjects. The structure of phasic clusters and the higher sparseness indicated that phasic synergies capture specific muscle activation patterns related to the movement while tonic synergies show co-contraction of multiple muscles for joint stabilization and holding postures. These results suggest that in many scenarios phasic and tonic synergies should be extracted separately, especially when performing muscle synergy analysis in patients with abnormal tonic activity and for tuning devices with gravity support.