Removing the stimulation artifacts evoked by the functional electrical stimulation (FES) in electromyogram (EMG) signals is a challenge. Previous researches on stimulation artifact removal have focused on FES modulation with time-constant parameters, which has limitations when there are time-variant parameters. Therefore, considering the synchronism of muscle activation induced by FES and the asynchronism of muscle activation induced by proprioceptive nerves, we proposed a novel adaptive spatial filtering method called G-S-G.

Efficient rehabilitation state evaluation is important to the design of rehabilitation strategies after stroke. However, most traditional evaluations have depended on subjective clinical scales, which do not entail quantitative evaluation of the motor function. Functional corticomuscular coupling (FCMC) can be used to quantitatively describe the rehabilitation state. However, how to apply FCMC to clinical evaluation still needs to be studied. In this study, we propose a visible evaluation model which can combine the FCMC indicators with a Ueda score to comprehensively evaluate the motor function. In this model, we first calculated the FCMC indicators based on our previous study, including transfer spectral entropy (TSE), wavelet package transfer entropy (WPTE) and multiscale transfer entropy (MSTE).

Motor control is a complex process of coordination and information interaction among neural, motor, and sensory functions. Investigating the correlation between motor-physiological information helps to understand the human motor control mechanisms and is important for the assessment of motor function status. In this manuscript, we investigated the differences in the neuromotor coupling analysis between healthy controls and stroke patients in different movements. We applied the corticokinematic coherence (CKC) function between the electroencephalogram (EEG) and acceleration (ACC) data.

Though coherence, a classical method to describe the linear correlation between two time series, has wide-ranging applications, from economics to neuroscience, it fails to illustrate the inherently multi-time scales-based correlations. In this paper, we proposed a multiscale-like coherence model, defined as composite multiscale coherence (CMSC) by combining the kth coarse-grain processing with the coherence. We made a comparison with the multiscale coherence (MSC) with coarse-grain process in numerical data to compare the sensitivity profiles to the coupling strength, data length and white Gaussian noise. After that, we applied the proposed model to explore the functional corticomuscular coupling (FCMC) by analyzing the correlation between the EEG and EMG signals.

A core issue in motor control is how the central nervous system generates and selects the muscle activation patterns necessary to achieve a variety of behaviors and movements. Extensive studies have verified that it is the foundation to induce a complex movement by the modular combinations of several muscles with a synergetic relationship. However, a few studies focus on the synergetic similarity and dissimilarity among different types of movements, especially for the upper extremity movements. In this study, we introduced the non-negative matrix factorization (NMF) method to explore the muscle activation patterns and synergy structure under 6 types of movements. The results showed a highly modular similarity of the muscle synergy among subjects under the same movement. Furthermore, Spearman's correlation analysis indicated significant similarities among HO-WE, HO-SU, and WE-SU (p < 0.001). Additionally, we also found shared synergy and special synergy in activation patterns among different movements. This study confirmed the theory of modular structure in the central nervous system, which yields a stable synergetic pattern under the same movement. Our findings on muscle synergy will be of great significance to motor control and even to clinical assessment techniques.

Stroke is one of the primary causes of motor disorders, which can seriously affect the patient's quality of life. However, the assessment of the upper limb affected by stroke is commonly based on scales, and the characteristics of brain reorganization induced by limb movement are not clear. Thus, this study aimed to investigate stroke-related cortical reorganization based on functional near infrared spectroscopy (fNIRS) during upper limb multi-joint linkage movement with reference to the Fugl-Meyer Assessment of the upper extremities (FMA-UE). The functional connectivity (FC) between channels and the regions of interest (ROI) was calculated by Pearson's correlation coefficient. The results showed that compared with the control group, the FC between the prefrontal cortex and the motor cortex was significantly increased in the resting state and the affected upper limb's multi-joint linkage movements, while the FC between the motor cortex was significantly decreased during the unaffected upper limb's multi-joint linkage movements.

The multiscale information interaction between the cortex and the corresponding muscles is of great significance for understanding the functional corticomuscular coupling (FCMC) in the sensory-motor systems. Though the multiscale transfer entropy (MSTE) method can effectively detect the multiscale characteristics between two signals, it lacks in describing the local frequency-band characteristics. Therefore, to quantify the multiscale interaction at local-frequency bands between the cortex and the muscles, we proposed a novel method, named bivariate empirical mode decomposition-MSTE (BMSTE), by combining the bivariate empirical mode decomposition (BEMD) with MSTE.

Brain–computer interface (BCI) based on motor imagery (MI) can control external applications by decoding different brain physiological signals, such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Traditional unimodal-based MI decoding methods cannot obtain satisfactory classification performance due to the limited representation ability in EEG or fNIRS signals. Usually, different brain signals have complementarity with different sensitivity to different MI patterns. To improve the recognition rate and generalization ability of MI, we propose a novel end-to-end multimodal multitask neural network (M2NN) model with the fusion of EEG and fNIRS signals. M2NN method integrates the spatial–temporal feature extraction module, multimodal feature fusion module, and multitask learning (MTL) module. Specifically, the MTL module includes two learning tasks, namely one main classification task for MI and one auxiliary task with deep metric learning.

Muscle synergy analysis is a kind of modularized decomposition of muscles during exercise controlled by the central nervous system (CNS). It can not only extract the synergistic muscles in exercise, but also obtain the activation states of muscles to reflect the coordination and control relationship between muscles. However, previous studies have mainly focused on the time-domain synergy without considering the frequency-specific characteristics within synergy structures. Therefore, this study proposes a novel method, named time-frequency non-negative matrix factorization (TF-NMF), to explore the time-varying regularity of muscle synergy characteristics of multi-channel surface electromyogram (sEMG) signals at different frequency bands. In this method, the wavelet packet transform (WPT) is used to transform the time-scale signals into time-frequency dimension. Then, the NMF method is calculated in each time-frequency window to extract the synergy modules.

Objective.Transcranial ultrasound stimulation (TUS), a large penetration depth and high spatial resolution technology, has developed rapidly in recent years. This study aimed to explore and evaluate the neuromodulation effects of TUS on mouse motor neural circuits under different parameters.Approach.Our study used functional corticomuscular coupling (FCMC) as an index to explore the modulation mechanism for movement control under different TUS parameters (intensity [Isppa] and stimulation duration). We collected local field potential (LFP) and tail electromyographic (EMG) data under TUS in healthy mice and then introduced the time-frequency coherence method to analyze the FCMC before and after TUS in the time-frequency domain. After that, we defined the relative coherence area to quantify the coherence between LFP and EMG under TUS.

It is difficult for stroke patients with flaccid paralysis to receive passive rehabilitation training. Therefore, virtual rehabilitation technology that integrates the motor imagery brain-computer interface and virtual reality technology has been applied to the field of stroke rehabilitation and has evolved into a physical rehabilitation training method. This virtual rehabilitation technology can enhance the initiative and adaptability of patient rehabilitation. To maximize the deep activation of the subjects motor nerves and accelerate the remodeling mechanism of motor nerve function, this study designed a brain-computer interface rehabilitation training strategy using different virtual scenes, including static scenes, dynamic scenes, and VR scenes.

Rapid serial visual presentation (RSVP) is a type of electroencephalogram (EEG) pattern commonly used for target recognition. Besides delta- and theta-band responses already used for classification, RSVP task also evokes gamma-band responses having low amplitude and large individual difference. This paper proposes a filter bank spatio-temporal component analysis (FBSCA) method, extracting spatio-temporal features of the gamma-band responses for the first time, to enhance the RSVP classification performance.

Objective. Complex biological systems consist of multi-level mechanism in terms of within- and cross-subsystems correlations, and they are primarily manifested in terms of connectivity, multiscale properties, and nonlinearity. Existing studies have each only explored one aspect of the functional corticocortical coupling (FCCC), which has some limitations in portraying the complexity of multivariable systems. The present study investigated the direct interactions of brain networks at multiple time scales.Approach. We extended the multivariate transfer entropy (MuTE) method and proposed a novel method, named multiscale multivariate transfer entropy (MSMVTE), to explore the direct interactions of brain networks across multiple time scale. To verify this aim, we introduced three simulation models and compared them with multiscale transfer entropy (MSTE) and MuTE methods. We then applied MSMVTE method to analyze FCCC during a unilateral right-hand steady-state force task.

Low-intensity focused ultrasound stimulation (LIFUS) has the potential to noninvasively penetrate the intact skull and to modulate neural activity in the cortex and deep brain nuclei. The midbrain periaqueductal gray (PAG) is associated with the generation of defensive behaviors. The aim of this study was to examine whether LIFUS of the PAG induced defensive behaviors in mice. A 3.8-MHz head-mounted ultrasound transducer with a small focus size (0.5 mm × 0.5 mm) was fabricated in house to precisely stimulate the free-moving mice. The corresponding behaviors were recorded in real time. Avoidance, flight, and freezing were used to assess ultrasound-induced defensive responses. The safety of LIFUS was examined via hematoxylin and eosin (H&E) staining and Nissl staining.

Functional corticomuscular coupling (FCMC) between the brain and muscles has been used for motor function assessment after stroke. Two types, iso-frequency coupling (IFC) and cross-frequency coupling (CFC), are existed in sensory-motor system for healthy people. However, in stroke, only a few studies focused on IFC between electroencephalogram (EEG) and electromyogram (EMG) signals, and no CFC studies have been found. Considering the intrinsic complexity and rhythmicity of the biological system, we first used the wavelet package transformation (WPT) to decompose the EEG and EMG signals into several subsignals with different frequency bands, and then applied transfer entropy (TE) to analyze the IFC and CFC relationship between each pair-wise subsignal.

Hand movement in humans is verified as asymmetries and lateralization, and two hemispheres make some distinct but complementary contributions in the control of hand movement. However, little research has been done on whether the information transfer of the motor system is different between left and right hand movement. Considering the importance of functional corticomuscular coupling (FCMC) between the motor cortex and contralateral muscle in movement assessment, this study aimed to explore the differences between left and right hand by investigating the interaction between muscle and brain activity. Here, we applied the transfer spectral entropy (TSE) algorithm to quantize the connection between electroencephalogram (EEG) over the brain scalp and electromyogram (EMG) from extensor digitorum (ED) and flexor digitorum superficialis (FDS) muscles recorded simultaneously during a gripping task.

Objective: Direct interaction between the brain and muscle is significant for investigating the oscillation mechanisms in the motor control system. Methods: To our knowledge, the partial directed coherence (PDC) method is sufficient to reflect the direct interaction among multivariate time series in the frequency domain, but fails to eliminate the spectral overlap among frequency bands. Therefore, we expanded the PDC method and constructed a novel method, named variational-mode-decomposition-based PDC (VMDPDC), to describe the direct interaction on specific frequency bands.