Study of the Correlation between the Motor Ability of the Individual Upper Limbs and Motor Imagery Induced Neural Activities.

Motor imagery based brain-computer interfaces (MI-BCIs) have excellent application prospects in motor enhancement and rehabilitation. However, MI-induced electroencephalogram features applied to MI-BCI usually vary from person to person. This study aimed to investigate whether the motor ability of the individual upper limbs was associated with these features, which helps understand the causes of inter-subject variability. We focused on the behavioral and psychological factors reflecting motor abilities. We first obtained the behavioral scale scores from Edinburgh Handedness Questionnaire, Maximum Grip Strength Test, and Purdue Pegboard Test assessments to evaluate the motor execution ability. We also required the subjects to complete the psychological Movement Imagery Questionnaire-3 estimate, representing MI ability. Then we recorded EEG signals from all twenty-two subjects during MI tasks. Pearson correlation coefficient and stepwise regression were used to analyze the relationships between MI-induced relative event-related desynchronization (rERD) patterns and motor abilities. Both Purdue Pegboard Test and Movement Imagery Questionnaire-3 scores had significant correlations with MI-induced neural oscillation patterns. Notably, the Purdue Pegboard Test of the left hand had the most significant correlation with the alpha rERD. The results of stepwise multiple regression analysis showed that the Purdue Pegboard Test and Movement Imagery Questionnaire-3 could best predict the MI-induced rERD. The results demonstrate that hand dexterity and fine motor coordination are significantly related to MI-induced neural activities. In addition, the method of imagining is also relevant to MI features. Therefore, this study is meaningful for understanding individual differences and the design of user-centered MI-BCI.

Incorporating EEG and fNIRS Patterns to Evaluate Cortical Excitability and MI-BCI Performance During Motor Training.

As electroencephalography (EEG) is nonlinear and nonstationary in nature, an imperative challenge for brain-computer interfaces (BCIs) is to construct a robust classifier that can survive for a long time and monitor the brain state stably. To this end, this research aims to improve BCI performance by incorporation of electroencephalographic and cerebral hemodynamic patterns. A motor imagery (MI)-BCI based visual-haptic neurofeedback training (NFT) experiment was designed with sixteen participants. EEG and functional near infrared spectroscopy (fNIRS) signals were simultaneously recorded before and after this transient NFT. Cortical activation was significantly improved after repeated and continuous NFT through time-frequency and topological analysis. A classifier calibration strategy, weighted EEG-fNIRS patterns (WENP), was proposed, in which elementary classifiers were constructed by using both the EEG and fNIRS information and then integrated into a strong classifier with their independent accuracy-based weight assessment. The results revealed that the classifier constructed on integrating EEG and fNIRS patterns was significantly superior to that only with independent information (  âˆ¼  10% and  âˆ¼  18% improvement respectively), reaching  âˆ¼  89% in mean classification accuracy. The WENP is a classifier calibration strategy that can effectively improve the performance of the MI-BCI and could also be used to other BCI paradigms. These findings validate that our proposed methods are feasible and promising for optimizing conventional motor training methods and clinical rehabilitation.

Motor Imagery and Action Observation Induced Electroencephalographic Activations to Guide Subject-Specific Training Paradigm: A Pilot Study.

Brain-computer interface (BCI)-based motor rehabilitation feedback training system can facilitate motor function reconstruction, but its rehabilitation mechanism with suitable training protocol is unclear, which affects the application effect. To this end, we probed the electroencephalographic (EEG) activations induced by motor imagery (MI) and action observation (AO) to provide an effective method to optimize motor feedback training. We grouped subjects according to their alpha-band sensorimotor cortical excitability under MI and AO conditions, and investigated the EEG response under the same paradigm between groups and different motor paradigms within group, respectively. The results showed that there were significant differences in sensorimotor activations between two groups of subjects. Specifically, the group with weaker MI induced EEG features, could achieve stronger sensorimotor activations in AO than that of other conditions. The group with stronger MI induced EEG features, could achieve stronger sensorimotor activations in the MI+AO than that of other conditions. We also explored their classification and brain network differences, which might try to explain the EEG mechanism in different individuals and help stroke patients to choose appropriate subject-specific motor training paradigm for their rehabilitation and better treatment outcomes.

Enhancing Visual-Guided Motor Imagery Performance via Sensory Threshold Somatosensory Electrical Stimulation Training.

OBJECTIVE: Motor imagery (MI) based brain- computer interface (BCI) has been widely studied as an effective way to enhance motor learning and promote motor recovery. However, the accuracy of MI-BCI heavily depends on whether subjects can perform MI tasks correctly, which largely limits the general application of MI-BCI. To overcome this limitation, a training strategy based on the combination of MI and sensory threshold somatosensory electrical stimulation (MI+st-SES) is proposed in this study. METHODS: Thirty healthy subjects were recruited and randomly divided into SES group and control group. Both groups performed left-hand and right-hand MI tasks in three consecutive blocks. The main difference between two groups lies in the second block, where subjects in SES group received the st-SES during MI tasks whereas the control group performed MI tasks only. RESULTS: The results showed that the SES group had a significant improvement in event-related desynchronization (ERD) of alpha rhythm after the training session of MI+st-SES (left-hand: F(2,27) = 9.98, p<0.01; right-hand: F(2, 27) = 10.43, p<0.01). The classification accuracy between left- and right-hand MI in the SES group was also significantly improved following MI+st-SES training (F(2,27) = 6.46, p<0.01). In contrary, there was no significant difference between the first and third blocks in the control group (F(2,27) = 0.18, p = 0.84). The functional connectivity based on weighted pairwise phase consistency (wPPC) over the sensorimotor area also showed an increase after the MI+st-SES training. CONCLUSION AND SIGNIFICANCE: Our findings indicate that training based on MI+st-SES is a promising way to foster MI performance and assist subjects in achieving efficient BCI control.

Effects of Transcutaneous Vagus Nerve Stimulation (tVNS) on Action Planning: A Behavioural and EEG Study.

Action planning is an important decision-making process, which can be specially affected by environment. Response selection during action planning has been demonstrated to be modulated by tVNS. Therefore, tVNS shows a great potential for modulating the action planning process. We aimed to explore the tVNS-induced effect on action planning in behavioural and electrophysiology. Twenty-eight participants were randomly divided into two groups (active group and sham group). A single-blind, sham-controlled between-subject design was applied to explore the effect of online-tVNS (i.e., tVNS overlapping with the task) on action planning paradigm. We measured and compared reaction time (RT) and movement-related cortical potentials (MRCPs) before and after tVNS between active and sham groups. As compared to sham group, for the ipsilateral hand/contralateral hemisphere relative to the stimulated side, active tVNS significantly reduced the reaction time and decreased the MRCP amplitude mainly in the challenging tasks. Our results indicate that tVNS can produce a lateralization effect on action planning, especially plays an important role in the more challenging tasks as reflected both in the behavioural and electrophysiological results.

Efficacy and Mechanism of Electroacupuncture Treatment of Rabbits With Different Degrees of Knee Osteoarthritis: A Study Based on Synovial Innate Immune Response.

Knee osteoarthritis (KOA) is a chronic degenerative bone and joint disease, which is often clinically manifested as pain, joint swelling, and deformity. Its pathological manifestations are mainly synovial inflammation and cartilage degeneration. This study aims to investigate the efficacy of electro-acupuncture (EA) on model rabbits with varying degrees of KOA and to study the mechanism of EA on KOA based on the innate immune response. Mild and moderate rabbit KOA models were established using a modified Hluth method, and EA was given to both the mild and moderate model groups. The Lequesne-MG index was used to evaluate the behavioral changes in the rabbits before and after EA treatment. Morphological changes in the synovial membrane and cartilage of each group were observed by H&E staining. The Mankin scoring standard and the Krenn scoring standard were used to score the pathology of the cartilage tissue and synovial tissue, respectively. The inflammatory factors and metalloproteinases were detected in the serum of each group by ELISA. The protein and messenger RNA (mRNA) expressions of important elements related to Toll-like receptors (TLRs)-mediated innate immune response in the synovial tissue were detected by Western blot and quantitative PCR (qPCR). The Lequesne-MG index score of the rabbits gradually increased with the modeling prolonged but decreased significantly after EA treatment, indicating that EA has a better effect on alleviating the pain and improving the dysfunction. The morphological analysis showed that the inflammation of and the damage to the synovial membrane and the cartilage tissue gradually deteriorated with the modeling prolonged. However, the synovial membrane inflammation was significantly relieved after EA treatment, and the cartilage injury showed signs of repair. The ELISA analysis showed that, with the modeling prolonged, the serum-related inflammatory factors and mechanism of metalloproteinases gradually increased but decreased after EA treatment. The tumor necrosis factor alpha (TNF-α), interleukin 6 (IL-6), and matrix metalloproteinase3 (MMP3) of EA1 group were significantly lower than those of EA2 group. Both Western blot and qPCR results showed that the protein and mRNA expressions of the elements related to the innate immune response in the synovial membrane increased gradually with the modeling prolonged, but decreased significantly after EA treatment. Additionally, the expression of some components in EA1 group was significantly lower than that in EA2 group. These results confirm that synovial inflammation gradually aggravated with time from the early to mid-stage of KOA. EA alleviated the inflammation and histological changes in KOA rabbits by inhibiting the TLRs-mediated innate synovial immune response. This suggests that using EA in the early stage of KOA may achieve a desirable efficacy.

[Research progress and prospect of collaborative brain-computer interface for group brain collaboration].

As the most common active brain-computer interaction paradigm, motor imagery brain-computer interface (MI-BCI) suffers from the bottleneck problems of small instruction set and low accuracy, and its information transmission rate (ITR) and practical application are severely limited. In this study, we designed 6-class imagination actions, collected electroencephalogram (EEG) signals from 19 subjects, and studied the effect of collaborative brain-computer interface (cBCI) collaboration strategy on MI-BCI classification performance, the effects of changes in different group sizes and fusion strategies on group multi-classification performance are compared. The results showed that the most suitable group size was 4 people, and the best fusion strategy was decision fusion. In this condition, the classification accuracy of the group reached 77%, which was higher than that of the feature fusion strategy under the same group size (77.31% vs. 56.34%), and was significantly higher than that of the average single user (77.31% vs. 44.90%). The research in this paper proves that the cBCI collaboration strategy can effectively improve the MI-BCI classification performance, which lays the foundation for MI-cBCI research and its future application.

EEG-controlled functional electrical stimulation rehabilitation for chronic stroke: system design and clinical application.

Stroke is one of the most serious diseases that threaten human life and health. It is a major cause of death and disability in the clinic. New strategies for motor rehabilitation after stroke are undergoing exploration. We aimed to develop a novel artificial neural rehabilitation system, which integrates brain-computer interface (BCI) and functional electrical stimulation (FES) technologies, for limb motor function recovery after stroke. We conducted clinical trials (including controlled trials) in 32 patients with chronic stroke. Patients were randomly divided into the BCI-FES group and the neuromuscular electrical stimulation (NMES) group. The changes in outcome measures during intervention were compared between groups, and the trends of ERD values based on EEG were analyzed for BCI-FES group. Results showed that the increase in Fugl Meyer Assessment of the Upper Extremity (FMA-UE) and Kendall Manual Muscle Testing (Kendall MMT) scores of the BCI-FES group was significantly higher than that in the sham group, which indicated the practicality and superiority of the BCI-FES system in clinical practice. The change in the laterality coefficient (LC) values based on µ-ERD (ΔLCm-ERD) had high significant positive correlation with the change in FMA-UE(r = 0.6093, P = 0.012), which provides theoretical basis for exploring novel objective evaluation methods.

Effect of Acupuncture on Ovary Morphology and Function in DHEA-Induced Polycystic Ovary Syndrome Model Rats.

OBJECTIVE: To investigate the effects of acupuncture on ovary morphology and function in dehydroepiandrosterone (DHEA)-induced polycystic ovary syndrome (PCOS) model rats. METHODS: A total of 40 adult female Wistar rats were randomly allocated to 4 groups by a random number table, including control, model, metformin and acupuncture groups, 10 rats in each group. PCOS rat model was developed by injecting with DHEA (6 mg/100 g body weight) in 0.2 mL of oil subcutaneously. Electrical stimulation (2 Hz, 3 mA) was applied to Guanyuan (CV 4), Zigong (EX-CA1) and Qihai (CV 6) acupoints for 30 min daily in the acupuncture group, and metformin (200 mg/kg) was given to rats in the metformin group, both once per day for 21 consecutive days, and rats in the normal group was fed with normal saline and fed regularly. After 21 days of administration, the rat blood samples were collected for detecting the reproductive hormonal levels [luteinizing hormone (LH), follicle stimulating hormone (FSH), estradiol (E2), progesterone (P), testosterone (T)] and inflammatory factors (visfatin, IL-6) analysis. Ovary tissue was used for histopathological analysis. RESULTS: Compared with the model group, rats in the acupuncture and metformin groups were significantly lower in weight gain, FSH, LH and T levels, and E2 and P levels significantly increased (alll P<0.05). Meanwhile, LH and FSH levels were significantly decreased, and P, T and E2 levels significantly increased in the acupuncture group, compared with the metformin group (P<0.05). Compared with the model group, IL-6 and visfatin levels were significantly decreased in the acupuncture and metformin groups (P<0.05). There were no significant differences in IL-6 and visfatin levels between the acupuncture and metformin groups (P>0.05). Ovarian diameter in the acupuncture and metformin groups were smaller than the model group (P<0.05). However, there were no significant differences in ovarian diameters between the acupuncture and metformin groups (P>0.05). CONCLUSION: Acupuncture might improve ovary morphology and its function in DHEA-induced PCOS model rats.

A BCI based visual-haptic neurofeedback training improves cortical activations and classification performance during motor imagery.

OBJECTIVE: We proposed a brain-computer interface (BCI) based visual-haptic neurofeedback training (NFT) by incorporating synchronous visual scene and proprioceptive electrical stimulation feedback. The goal of this work was to improve sensorimotor cortical activations and classification performance during motor imagery (MI). In addition, their correlations and brain network patterns were also investigated respectively. APPROACH: 64-channel electroencephalographic (EEG) data were recorded in nineteen healthy subjects during MI before and after NFT. During NFT sessions, the synchronous visual-haptic feedbacks were driven by real-time lateralized relative event-related desynchronization (lrERD). MAIN RESULTS: By comparison between previous and posterior control sessions, the cortical activations measured by multi-band (i.e. alpha_1: 8-10 Hz, alpha_2: 11-13 Hz, beta_1: 15-20 Hz and beta_2: 22-28 Hz) absolute ERD powers and lrERD patterns were significantly enhanced after the NFT. The classification performance was also significantly improved, achieving a ~9% improvement and reaching ~85% in mean classification accuracy from a relatively poor performance. Additionally, there were significant correlations between lrERD patterns and classification accuracies. The partial directed coherence based functional connectivity (FC) networks covering the sensorimotor area also showed an increase after the NFT. SIGNIFICANCE: These findings validate the feasibility of our proposed NFT to improve sensorimotor cortical activations and BCI performance during motor imagery. And it is promising to optimize conventional NFT manner and evaluate the effectiveness of motor training.

BCI Monitor Enhances Electroencephalographic and Cerebral Hemodynamic Activations During Motor Training.

Motor imagery-based brain-computer interface (MI-BCI) controlling functional electrical stimulation (FES) is promising for disabled patients to restore their motor functions. However, it remains unclear how much the BCI part can contribute to the functional coupling between the brain and muscle. Specifically, whether it can enhance the cerebral activation for motor training? Here, we investigate the electroencephalographic and cerebral hemodynamic responses for MI-BCI-FES training and MI-FES training, respectively. Twelve healthy subjects were recruited in the motor training study when concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were recorded. Compared with the MI-FES training conditions, the MI-BCI-FES could induce significantly stronger event-related desynchronization (ERD) and blood oxygen response, which demonstrates that BCI indeed plays a functional role in the closed-loop motor training. Therefore, this paper verifies the feasibility of using BCI to train motor functions in a closed-loop manner.

A visual-haptic neurofeedback training improves sensorimotor cortical activations and BCI performance.

Neurofeedback training (NFT) could provide a novel way to investigate or restore the impaired brain function and neuroplasticity. However, it remains unclear how much the different feedback modes can contribute to NFT training. Specifically, whether they can enhance the cortical activations for motor training. To this end, our study proposed a brain-computer interface (BCI) based visual-haptic NFT incorporating synchronous visual scene and proprioceptive electrical stimulation feedback. By comparison between previous and posterior control sessions, the cortical activations measured by multi-band (i.e. alpha_1: 8-10Hz, alpha_2: 11-13Hz, beta_1: 15-20Hz and beta_2: 22-28Hz) lateralized relative event-related desynchronization (lrERD) patterns were significantly enhanced after NFT. And the classification performance was also significantly improved, achieving a ~9% improvement and reaching ~85% in mean classification accuracy from a relatively low MI-BCI performance. These findings validate the feasibility of our proposed visual- haptic NFT approach to improve sensorimotor cortical activations and BCI performance during motor training.

Cortical Activations and BCI Performances at Different Speeds of Visual and Proprioceptive Stimulation.

Motor imagery based brain-computer interface (MI-BCI) is one of the most common paradigms utilized in neurofeedback training (NFT) for rehabilitation engineering. Specifically, finding an appropriate feedback protocol is significantly important to improve the effectiveness of the motor training system. To this end, we investigated the electroencephalography(EEG) oscillatory patterns measured by event-related desynchronization (ERD) when sixteen participants accepted the visual and proprioceptive stimulation achieving the kinematic hand grasping movements at three different speeds (i.e. 1/3 Hz, 2/3 Hz and 1 Hz). The EEG results indicated that the ERD patterns showed no significant difference in sensorimotor cortex (i.e. C3 and FC3 channels) by comparing the three conditions. Nevertheless, the 2/3 Hz stimulation speed could achieve a significantly better classification performance than the other two conditions across all participants. Therefore, the visual and proprioceptive electrical stimulation achieving the kinematic hand grasping at 2/3 Hz speed might provide an available approach for the online MI-BCI system based NFT system in the future.

Direct spectroscopic evidence of CO spillover and subsequent reaction with preadsorbed NOx on Pd and K cosupported Mg-Al mixed oxides.

The CO adsorption and subsequent reaction with preadsorbed NO(x) on Pd and K cosupported Mg-Al mixed oxides (Pd-K/MgAlO, 1/8/100 w/w) were investigated using in situ FTIR spectroscopy. During CO adsorption, a peculiar and well-defined IR band at 2160 cm(-1) was observed. Several elaborately designed experiments such as the competitive adsorption of CO and CO(2) demonstrated that the 2160 cm(-1) band was exclusively assigned to a carbonyl species on K sites due to the CO spillover from Pd to K, which results from a strong Pd-K interaction based on temperature-programmed reduction with H(2) experiments. Importantly, the spillover of CO is found to be involved in the reduction of preadsorbed NO(x) from temperature-programmed surface reactions with CO. Thus, all adsorbed NO(x) can be reduced by CO before desorption. Like the process of "pumping" CO by Pd from the atmosphere to "irrigate the field" of the nitrates/nitrites, the adsorbed NO(x) at not only K sites adjacent to Pd but also at the remote K sites can be reduced into N(2) and N(2)O effectively.