Tailoring brain-machine interface rehabilitation training based on neural reorganization: towards personalized treatment for stroke patients.

Electroencephalogram (EEG)-based brain-machine interface (BMI) has the potential to enhance rehabilitation training efficiency, but it still remains elusive regarding how to design BMI training for heterogeneous stroke patients with varied neural reorganization. Here, we hypothesize that tailoring BMI training according to different patterns of neural reorganization can contribute to a personalized rehabilitation trajectory. Thirteen stroke patients were recruited in a 2-week personalized BMI training experiment. Clinical and behavioral measurements, as well as cortical and muscular activities, were assessed before and after training. Following treatment, significant improvements were found in motor function assessment. Three types of brain activation patterns were identified during BMI tasks, namely, bilateral widespread activation, ipsilesional focusing activation, and contralesional recruitment activation. Patients with either ipsilesional dominance or contralesional dominance can achieve recovery through personalized BMI training. Results indicate that personalized BMI training tends to connect the potentially reorganized brain areas with event-contingent proprioceptive feedback. It can also be inferred that personalization plays an important role in establishing the sensorimotor loop in BMI training. With further understanding of neural rehabilitation mechanisms, personalized treatment strategy is a promising way to improve the rehabilitation efficacy and promote the clinical use of rehabilitation robots and other neurotechnologies.

Slackline training for Paralympic alpine sit skiers: Development of human-device multi-joint coordination.

PURPOSE: Para-alpine sit skiers face unique challenges in balance control due to their disabilities and the use of sit skis. This study assessed their multi-joint coordination before and after slackline training. METHODS: Nine alpine sit skiers (6 M/3 F; 27 ± 8 years; height: 168.3 ± 6.0 cm; body mass: 55.4 ± 6.9 kg) with different disabilities (LW10-LW12) volunteered for the experiment. All subjects performed slackline training for 5 weeks (20 sessions). Joint kinematics were captured by vision-based markerless motion analysis. Root mean square (RMS) amplitude, mean velocity and mean power frequency (MPF) were evaluated. RESULTS: After training, performance improved significantly with an increase in balance time (1041%, p = 0.002), and a decrease in joint angular velocities and RMS amplitude of the sit ski foot (p < 0.05). Joint synergies were developed through in- or anti-phase movements between joint pairs, particularly involving the hip joints (continuous relative phase angles ~0° or 180°, p < 0.001). Multi-joint coordination shifted from large-RMS amplitude of elbows to low-MPF large-RMS amplitude of the hip and shoulders (p < 0.05), with a significant increase of hip weighting (77.61%, p = 0.031) in the principal component analysis. The coordination was maintained with the change of slackline tension (p < 0.05). Athletes with severe trunk disabilities (LW10) had shorter balance time and poorer coordination than athletes with full trunk functions (LW12). CONCLUSIONS: Our findings showed the development of joint coordination involving better control of the hip and sit skis during the challenging slackline training task.

Cluster analysis of multiple impairment measures in evidence-based classification for para-alpine sit skiers.

The International Paralympic Committee has been promoting the development of evidence-based classification to reduce the subjectivity in current decision-making systems. The current study aimed to evaluate the validity of the impairment and performance tests for para-alpine sit skiing classification, and whether cluster analysis of the measures would produce a valid classification structure. Thirty-eight para-alpine sit skiers with different disabilities completed seven tests. During these tests, isometric trunk strength, trunk muscle excitation, trunk range of movement (ROM), and simulated skiing performance (board tilt angle) were assessed. Correlations between the measures and the board tilt angle were calculated. To group athletes, K-means cluster analysis was performed according to how much the impairment measures affected the board tilting. There were significant correlations between all measures and the maximal board tilt angle (r = 0.35-0.81, p < 0.05). The cluster analysis revealed that the introduction of ROM and muscle excitation was an effective supplement to strength measures in improving the classification accuracy (53%-79%). It produced four clusters with strong structures (mean silhouette coefficient = 0.81) and large and significant inter-cluster differences in most measures and performance between clusters (p < 0.05). The cluster analysis produced classes comprising athletes with similar degrees of activity limitation. All tests reported can help establish a more transparent classification system for para-alpine sit skiers. This study also provides a reference for evidence-based classification systems in other Para sports.

Lesion-specific cortical activation following sensory stimulation in patients with subacute stroke.

BACKGROUND: Sensory stimulation can play a fundamental role in the activation of the primary sensorimotor cortex (S1-M1), which can promote motor learning and M1 plasticity in stroke patients. However, studies have focused mainly on investigating the influence of brain lesion profiles on the activation patterns of S1-M1 during motor tasks instead of sensory tasks. Therefore, the objective of this study is to explore the lesion-specific activation patterns due to different brain lesion profiles and types during focal vibration (FV). METHODS: In total 52 subacute stroke patients were recruited in this clinical experiment, including patients with basal ganglia hemorrhage/ischemia, brainstem ischemia, other subcortical ischemia, cortical ischemia, and mixed cortical-subcortical ischemia. Electroencephalograms (EEG) were recorded following a resting state lasting for 4 min and three sessions of FV. FV was applied over the muscle belly of the affected limb's biceps for 3 min each session. Beta motor-related EEG power desynchronization overlying S1-M1 was used to indicate the activation of S1-M1, while the laterality coefficient (LC) of the activation of S1-M1 was used to assess the interhemispheric asymmetry of brain activation. RESULTS: (1) Regarding brain lesion profiles, FV could lead to the significant activation of bilateral S1-M1 in patients with basal ganglia ischemia and other subcortical ischemia. The activation of ipsilesional S1-M1 in patients with brainstem ischemia was higher than that in patients with cortical ischemia. No activation of S1-M1 was observed in patients with lesions involving cortical regions. (2) Regarding brain lesion types, FV could induce the activation of bilateral S1-M1 in patients with basal ganglia hemorrhage, which was significantly higher than that in patients with basal ganglia ischemia. Additionally, LC showed no significant correlation with the modified Barthel index (MBI) in all patients, but a positive correlation with MBI in patients with basal ganglia lesions. CONCLUSIONS: These results reveal that sensory stimulation can induce lesion-specific activation patterns of S1-M1. This indicates FV could be applied in a personalized manner based on the lesion-specific activation of S1-M1 in stroke patients with different lesion profiles and types. Our study may contribute to a better understanding of the underlying mechanisms of cortical reorganization.

Brain oscillatory activity correlates with the relief of post-stroke spasticity following focal vibration.

BACKGROUND: Some evidence has demonstrated that focal vibration (FV) contributes to the relief of post-stroke spasticity (PSS). Although the changes of cortical activity correlating with the relief of PSS induced by FV have been explored using transcranial magnetic stimulation, brain oscillatory activity during the above-mentioned process has not been fully understood. OBJECTIVE: The main purpose of this study is to explore the correlation between the changes in brain oscillatory activity and the relief of PSS following FV. METHODS: A clinical experiment was carried out, in which FV (87 Hz, 0.28 mm) was applied over the antagonist muscle's belly of the spastic muscle of ten chronic spastic stroke patients. An electroencephalogram was recorded following before-FV and three sessions of FV. Muscle properties to assess the relief of PSS were tested before-FV and immediately after three sessions of FV. RESULTS: EEG analysis has shown that FV can lead to the significant decrease in the relative power at C3 and C4 in the beta1 (13, 18 Hz), as well as C3 and C4 in the beta3 band (21, 30 Hz), indicating the activation of primary sensorimotor cortex (S1-M1). Muscle properties analysis has shown that, in the state of flexion of spastic muscle, muscle compliance and muscle displacement of the spastic muscle significantly increased right after FV, illustrating the relief of the spasticity. Moreover, the increase of muscle compliance is positively correlated with the reduction of difference index of the activation of bilateral S1-M1. CONCLUSIONS: This finding indicated that the relief of PSS can be associated with the activation of bilateral S1-M1 where the activation of the ipsilesional S1-M1 was higher than that of the contralesional one. This study showed the brain oscillatory activity in the bilateral S1-M1 correlating with the relief of PSS following FV, which could contribute to establishing cortex oscillatory activity as a biomarker of the relief of PSS and providing a potential mechanism explanation of the relief of PSS.

Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal.

The recorded electroencephalography (EEG) signal is often contaminated with different kinds of artifacts and noise. Singular spectrum analysis (SSA) is a powerful tool for extracting the brain rhythm from a noisy EEG signal. By analyzing the frequency characteristics of the reconstructed component (RC) and the change rate in the trace of the Toeplitz matrix, it is demonstrated that the embedding dimension is related to the frequency bandwidth of each reconstructed component, in consistence with the component mixing in the singular value decomposition step. A method for selecting the embedding dimension is thereby proposed and verified by simulated EEG signal based on the Markov Process Amplitude (MPA) EEG Model. Real EEG signal is also collected from the experimental subjects under both eyes-open and eyes-closed conditions. The experimental results show that based on the embedding dimension selection method, the alpha rhythm can be extracted from the real EEG signal by the adaptive SSA, which can be effectively utilized to distinguish between the eyes-open and eyes-closed states.

[Human-robot global Simulink modeling and analysis for an end-effector upper limb rehabilitation robot].

Robot rehabilitation has been a primary therapy method for the urgent rehabilitation demands of paralyzed patients after a stroke. The parameters in rehabilitation training such as the range of the training, which should be adjustable according to each participant's functional ability, are the key factors influencing the effectiveness of rehabilitation therapy. Therapists design rehabilitation projects based on the semiquantitative functional assessment scales and their experience. But these therapies based on therapists' experience cannot be implemented in robot rehabilitation therapy. This paper modeled the global human-robot by Simulink in order to analyze the relationship between the parameters in robot rehabilitation therapy and the patients' movement functional abilities. We compared the shoulder and elbow angles calculated by simulation with the angles recorded by motion capture system while the healthy subjects completed the simulated action. Results showed there was a remarkable correlation between the simulation data and the experiment data, which verified the validity of the human-robot global Simulink model. Besides, the relationship between the circle radius in the drawing tasks in robot rehabilitation training and the active movement degrees of shoulder as well as elbow was also matched by a linear, which also had a remarkable fitting coefficient. The matched linear can be a quantitative reference for the robot rehabilitation training parameters.

Enhancing wavefront estimation accuracy by using higher-order iterative compensations in the Southwell configuration.

Accurate wavefront integration based on gradient fields is crucial for various indirect measurement techniques, such as Shack-Hartmann sensing, shearography, and the fringe reflection technique. In this paper, a higher-order iterative compensation algorithm is proposed to enhance the reconstruction accuracy for the finite-difference-based least-squares integration (FLI) method. In this method, higher-order gradient fields are reconstructed and the calculated residual gradient fields compensate the truncation error with the traditional FLI by iterations. A comparison of different FLI methods, including traditional FLI, iterative FLI, higher-order FLI, and the proposed FLI method, is conducted. The result shows that the reconstructed wavefront with the proposed method is more accurate than those with other FLI methods. In addition, the impact of the gradient measurement noise is also discussed.

Influence of Amputation on Kinetic Chain Musculature Activity During Basic and Modified Core Exercises.

PURPOSE: Core strength is vital for athletic performance, and many more exercises that involve the kinetic chain have been designed for able-bodied athletes. Disabilities that impair the kinetic chain can reduce the effectiveness of strength training. However, the impact of amputation on core strength training of people with disabilities and its underlying mechanism remains unclear. This study aimed to evaluate the muscle activation patterns and levels in athletes with amputation during 4 basic and modified weight-bearing core strength-training exercises. METHODS: Fifteen elite athletes with unilateral amputation (170.6 [7.3] cm; 63.9 [11.9] kg; 25.9 [5.3] y) volunteered for this study. Surface electromyography was used to measure the muscle activity mainly in the lumbopelvic-hip complex-stabilizing muscles during 4 kinetic chain trunk exercises with and without modifications. RESULTS: The significance level was set at α = .05. The results showed a significant difference in muscle activation between different body sides (P < .05). Specifically, amputation on the support position resulted in a diagonal pattern of muscle activation, and amputation on the free distal segments resulted in a unilateral dominant pattern with higher activation in muscles on the nonamputated side (P < .05). Modifications led to significant decreases in muscle activation asymmetry index (P < .05). CONCLUSIONS: Amputation caused muscle activation asymmetry and 2 activation patterns. Modifications by enhancing proximal stability and adjusting distal loading effectively reduced the asymmetry of muscle activation. Coaches and clinicians can use these results to tailor exercises for athletes with disabilities in training and rehabilitation.

Adverse effects of unilateral transfemoral amputation on para-alpine sit skiers and mitigation methods.

OBJECTIVES: This study aimed to evaluate the adverse effects of unilateral transfemoral amputation on neuromuscular and kinematic parameters in alpine sit skiers, and to determine if additional restraints on the human-bucket interface could help mitigate the effects. DESIGN: Cross-sectional, repeated measures study. METHODS: Simulated skiing tests were conducted indoors involving 10 skiers with unilateral transfemoral amputation and 10 able-bodied participants. A Paralympic silver medalist performed slalom skiing tests on snow. These tests were conducted with and without additional strapping on the residual limb. Surface electromyography of trunk muscles and athletic performance was measured, and the asymmetry index was calculated. RESULTS: Athletes were significantly dependent on muscle activation on the dominant side (asymmetry index = 7.8 %-28.3 %, p < 0.05). Worse athletic performance to the dominant side was found based on inclination angles of the indoor board (asymmetry index = -9.8 %, p = 0.014) and outdoor sit ski (-11.1 %, p = 0.006), and distance to the gate poles during skiing turns (18.6 %, p < 0.001). After using additional restraints, the above asymmetry index declined significantly (asymmetry index < 4.5 %, p < 0.05). Furthermore, athletic performance was significantly improved on both body sides by 11.1 %-30.7 % (p < 0.05). CONCLUSIONS: Unilateral transfemoral amputation caused the dependence on the trunk muscles of the dominant side and the corresponding unilateral poor performance in athletes. Adjusting restraints in the human-equipment interface by additional strapping could mitigate the asymmetry issues and improve athletic performance.

Enhancing Brain-Computer Interface Performance by Incorporating Brain-to-Brain Coupling.

Human cooperation relies on key features of social interaction in order to reach desirable outcomes. Similarly, human-robot interaction may benefit from integration with human-human interaction factors. In this paper, we aim to investigate brain-to-brain coupling during motor imagery (MI)-based brain-computer interface (BCI) training using eye-contact and hand-touch interaction. Twelve pairs of friends (experimental group) and 10 pairs of strangers (control group) were recruited for MI-based BCI tests concurrent with electroencephalography (EEG) hyperscanning. Event-related desynchronization (ERD) was estimated to measure cortical activation, and interbrain functional connectivity was assessed using multilevel statistical analysis. Furthermore, we compared BCI classification performance under different social interaction conditions. In the experimental group, greater ERD was found around the contralateral sensorimotor cortex under social interaction conditions compared with MI without any social interaction. Notably, EEG channels with decreased power were mainly distributed around the frontal, central, and occipital regions. A significant increase in interbrain coupling was also found under social interaction conditions. BCI decoding accuracies were significantly improved in the eye contact condition and eye and hand contact condition compared with the no-interaction condition. However, for the strangers' group, no positive effects were observed in comparisons of cortical activations between interaction and no-interaction conditions. These findings indicate that social interaction can improve the neural synchronization between familiar partners with enhanced brain activations and brain-to-brain coupling. This study may provide a novel method for enhancing MI-based BCI performance in conjunction with neural synchronization between users.

Multiscale Canonical Coherence for Functional Corticomuscular Coupling Analysis.

Functional corticomuscular coupling (FCMC) probes multi-level information communication in the sensorimotor system. The canonical Coherence (caCOH) method has been leveraged to measure the FCMC between two multivariate signals within the single scale. In this paper, we propose the concept of multiscale canonical Coherence (MS-caCOH) to disentangle complex multi-layer information and extract coupling features in multivariate spaces from multiple scales. Then, we verified the reliability and effectiveness of MS-caCOH on two types of data sets, i.e., a synthetic multivariate data set and a real-world multivariate data set. Our simulation results showed that compared with caCOH, MS-caCOH enhanced coupling detection and achieved lower pattern recovery errors at multiple frequency scales. Further analysis on experimental data demonstrated that the proposed MS-caCOH method could also capture detailed multiscale spatial-frequency characteristics. This study leverages the multiscale analysis framework and multivariate method to give a new insight into corticomuscular coupling analysis.

Motor-Respiratory Coupling Improves Endurance Performance during Rhythmic Isometric Handgrip Exercise.

PURPOSE: This study aimed to evaluate whether motor-respiratory coupling exists in rhythmic isometric handgrip exercises and its effect on endurance performance. In addition, the mechanism underlying observed effects was to be investigated if higher motor-respiratory coupling rate could enhance endurance performance. METHODS: Eleven subjects completed three rhythmic isometric handgrip trials to task failure in a randomized manner. After one pretraining session to determine personal grip frequency, one trial was performed without respiration requirement (CON), and two trials were performed with inspiration-motor coupling (IMC) or expiration-motor coupling. Changes in maximal voluntary contraction (MVC) and EMG were used to measure neuromuscular fatigue. Force data during test were used to assess exercise intensity. Another 10 subjects completed electrical stimulation-induced finger flexion and extension during normal inspiration, normal expiration, fast inspiration, fast expiration, and breath holding. Force changes of different breathing conditions were compared. RESULTS: Normalized exercise time to exhaustion was significantly longer in IMC (1.27 ± 0.23) compared with expiration-motor coupling (0.82 ± 0.18) and CON (0.91 ± 0.18, P < 0.001). ΔMVC, grip frequency, force, and EMG indices were not different among conditions (all P > 0.05). Electrical stimulation-induced finger extensor force was significant higher during fast inspiration (1.11 ± 0.09) than normal respiration (1.00 ± 0.05) and fast expiration (0.94 ± 0.08, P < 0.05). CONCLUSIONS: IMC is an effective way to improve endurance performance of rhythmic handgrip exercise. This is likely due to a reduction in the energy consumption of motion control, as evidenced by similar peripheral fatigue in different conditions and modulation of corticospinal excitability by respiration.

Alpha tACS on Parieto-Occipital Cortex Mitigates Motion Sickness Based on Multiple Physiological Observation.

Approximately one third of the population is prone to motion sickness (MS), which is associated with the dysfunction in the integration of sensory inputs. Transcranial alternating current stimulation (tACS) has been widely used to modulate neurological functions by affecting neural oscillation. However, it has not been applied in the treatment of motion sickness. This study aims to investigate changes in brain oscillations during exposure to MS stimuli and to further explore the potential impact of tACS with the corresponding frequency and site on MS symptoms. A total of 19 subjects were recruited to be exposed to Coriolis stimuli to complete an inducing session. After that, they were randomly assigned to tACS stimulation group or sham stimulation group to complete a stimulation session. Electroencephalography (EEG), electrocardiogram, and galvanic skin response were recorded during the experiment. All the subjects suffering from obvious MS symptoms after inducing session were observed that alpha power of four channels of parieto-occipital lobe significantly decreased (P7: t =3.589, p <0.001; P8: t =2.667, p <0.05; O1: t =3.556, p <0.001; O2: t =2.667, p <0.05). Based on this, tACS group received the tACS stimulation at 10Hz from Oz to CPz. Compared to sham group, tACS stimulation significantly improved behavioral performance and entrained the alpha oscillation in individuals whose alpha power decrease during the inducing session. The findings show that parieto-occipital alpha oscillation plays a critical role in the integration of sensory inputs, and alpha tACS on parieto-occipital can become a potential method to mitigate MS symptoms.

Explainable Deep-Learning Prediction for Brain-Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion.

Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the relationship between cortical neural activity and motor recovery. EEG recorded in different states could more accurately predict motor recovery than single-state recordings. Here, we design a multi-state (combining eyes closed, EC, and eyes open, EO) fusion neural network for predicting the motor recovery of patients with stroke after EEG-brain-computer-interface (BCI) rehabilitation training and use an explainable deep learning method to identify the most important features of EEG power spectral density and functional connectivity contributing to prediction. The prediction accuracy of the multi-states fusion network was 82%, significantly improved compared with a single-state model. The neural network explanation result demonstrated the important region and frequency oscillation bands. Specifically, in those two states, power spectral density and functional connectivity were shown as the regions and bands related to motor recovery in frontal, central, and occipital. Moreover, the motor recovery relation in bands, the power spectrum density shows the bands at delta and alpha bands. The functional connectivity shows the delta, theta, and alpha bands in the EC state; delta, theta, and beta mid at the EO state are related to motor recovery. Multi-state fusion neural networks, which combine multiple states of EEG signals into a single network, can increase the accuracy of predicting motor recovery after BCI training, and reveal the underlying mechanisms of motor recovery in brain activity.

Tetramethylpyrazine-loaded electroconductive hydrogels promote tissue repair after spinal cord injury by protecting the blood-spinal cord barrier and neurons.

Spinal cord injury (SCI) usually induces profound microvascular dysfunction. It disrupts the integrity of the blood-spinal cord barrier (BSCB), which could trigger a cascade of secondary pathological events that manifest as neuronal apoptosis and axonal demyelination. These events can further lead to irreversible neurological impairments. Thus, reducing the permeability of the BSCB and maintaining its substructural integrity are essential to promote neuronal survival following SCI. Tetramethylpyrazine (TMP) has emerged as a potential protective agent for treating the BSCB after SCI. However, its therapeutic potential is hindered by challenges in the administration route and suboptimal bioavailability, leading to attenuated clinical outcomes. To address this challenge, traditional Chinese medicine, TMP, was used in this study to construct a drug-loaded electroconductive hydrogel for synergistic treatment of SCI. A conductive hydrogel combined with TMP demonstrates good electrical and mechanical properties as well as superior biocompatibility. Furthermore, it also facilitates sustained local release of TMP at the implantation site. Furthermore, the TMP-loaded electroconductive hydrogel could suppress oxidative stress responses, thereby diminishing endothelial cell apoptosis and the breakdown of tight junction proteins. This concerted action repairs BSCB integrity. Concurrently, myelin-associated axons and neurons are protected against death, which meaningfully restore neurological functions post spinal cord injury. Hence, these findings indicate that combining the electroconductive hydrogel with TMP presents a promising avenue for potentiating drug efficacy and synergistic repair following SCI.

Enhancement of EEG-EMG coupling detection using corticomuscular coherence with spatial-temporal optimization.

Objective. Corticomuscular coherence (CMC) is widely used to detect and quantify the coupling between motor cortex and effector muscles. It is promisingly used in human-machine interaction (HMI) supported rehabilitation training to promote the closed-loop motor control for stroke patients. However, suffering from weak coherence features and low accuracy in contingent neurofeedback, its application to HMI rehabilitation robots is currently limited. In this paper, we propose the concept of spatial-temporal CMC (STCMC), which is the coherence by refining CMC with delay compensation and spatial optimization.Approach. The proposed STCMC method measures the coherence between electroencephalogram (EEG) and electromyogram (EMG) in the multivariate spaces. Specifically, we combined delay compensation and spatial optimization to maximize the absolute value of the coherence. Then, we tested the reliability and effectiveness of STCMC on neurophysiological data of force tracking tasks.Main results. Compared with CMC, STCMC not only enhanced the coherence significantly between brain and muscle signals, but also produced higher classification accuracy. Further analysis showed that temporal and spatial parameters estimated by the STCMC reflected more detailed brain topographical patterns, which emphasized the different roles between the contralateral and ipsilateral hemisphere.Significance. This study integrates delay compensation and spatial optimization to give a new perspective for corticomuscular coupling analysis. It is also feasible to design robotic neurorehabilitation paradigms by the proposed method.

Recognizing the individualized sensorimotor loop of stroke patients during BMI-supported rehabilitation training based on brain functional connectivity analysis.

BACKGROUND: Electroencephalogram (EEG) based brain-machine interaction training can facilitate rehabilitation by closing the sensorimotor loop. However, it remains unclear how to evaluate whether the loop is closed, especially for stroke patients whose brain regions of motor control and sensorimotor feedback could be altered. Our hypothesis is that motor recovery depends on whether sensorimotor loop is established poststroke. This study aims to explore how to evaluate the establishment of sensorimotor loop based on the evolving neural reorganization patterns after stroke. NEW METHOD: 14 stroke patients participated in the experiment and EEG were recorded during three specific tasks: Movement Imagery (MI), Passive Movement (PM) and Movement Execution (ME). Activated brain regions correlated with movement intention expression and sensorimotor feedback were detected respectively during MI and PM. In ME, local-averaged Phase Lag Index (PLI) was analyzed to represent the functional connectivity between activated brain regions of MI and PM. RESULTS: Individualized cortical activation was found both in MI and PM. The overlapping brain activation during PM and MI did not correlate with patient's Fugl-Meyer Upper Extremity Motor Score (FMU) . However, we found that FMU of the group with higher local-averaged PLI was statistically higher than FMU of the group with lower local-averaged PLI compared with global-averaged PLI (p < 0.05). CONCLUSIONS: The findings demonstrate functional connectivity between activated brain regions of motor control and sensorimotor feedback may imply if the individualized sensorimotor loop is established poststroke. The successful formation of the closed loop can indicate stroke patients' motor recovery.

EEG-based evaluation of motion sickness and reducing sensory conflict in a simulated autonomous driving environment.

Autonomous driving offers significant potential for changes in the automotive industry. However, sensory conflict during autonomous driving can lead to motion sickness. Quantitative evaluation and effective preventions to predict and reduce motion sickness are needed. The goal of this study is to verify the objective indicator of motion sickness level based on encephalography (EEG) that we proposed before and investigate the influence of attenuating sensory conflict on motion sickness. A 6-degree of freedom (DOF) driving simulator platform was used to provide an autonomous driving environment to the subjects, and the subjective motion sickness level (MSL), as well as the EEG signals of 15 healthy subjects, were collected simultaneously during 3 conditions, i) autonomous driving, ii) autonomous driving with eyes blindfolded and iii) active driving. The MSLs were reported by the subjects every two minutes, providing a reference to the recorded EEG signals. The EEG signals were analyzed and compared among different conditions. Average MSLs were higher in autonomous driving than in autonomous driving with eyes blindfolded and active driving, together with the increase of the mean EEG frequency of theta band in the central, parietal and occipital areas (FC5, Cz, CP5, P3, and POz). These findings validated that EEG mean frequency of theta band could be an indicator of motion sickness, besides an attenuated visual input or active control of the vehicle can effectively reduce the generation of motion sickness.

Size-Controllable Eu-MOFs through Machine Learning Technology: Application for High Sensitive Ions and Small-Molecular Identification.

Metal-organic frameworks (MOFs) with the aggregation-induced emission (AIE) activities exhibit potential applications in the fields of energy and biomedical technology. However, the controllable synthesis of MOFs in the varied particle sizes not only affects their AIE activities, but also restricts their application scenarios. In this work, the varied particle sizes of Eu-MOFs are synthesized by adjusting the synthesis process parameters, and their variation rules combining the single factor analysis method with machine learning technology are studied. Based on the R2 score, the gradient boosting decision tree (GBDT) regression model (0.9535) is employed to calculate the weight and correlation between different synthesis process parameters and it is shown that all these parameters have synergic effects on the particle sizes of Eu-MOFs, and the Eu-precursors concentration dominates in their synthesis process. Furthermore, it is indicated that the large size of Eu-MOFs and strong structural stability contribute to their high AIE activities. Finally, a screen-printed pattern is fabricated using the sample of "120-0.3-6," and this pattern exhibits a bright red fluorescence under the UV light. More importantly, this kind of Eu-MOFs can also be used to identify varied ions (Fe3+ , F- , I- , SO42- , CO32- , and PO43- ) and citric acid.