Within-Session Reliability of fNIRS in Robot-Assisted Upper-Limb Training.

Functional near-infrared spectroscopy (fNIRS) seems opportune for neurofeedback in robot-assisted rehabilitation training due to its noninvasive, less physical restriction, and no electromagnetic disturbance. Previous research has proved the cross-session reliability of fNIRS responses to non-motor tasks (e.g., visual stimuli) and fine-motor tasks (e.g., finger tapping). However, it is still unknown whether fNIRS responses remain reliable 1) in gross-motor tasks, 2) within a training session, and 3) for different training parameters. Hence, this study aimed to investigate the within-session reliability of fNIRS responses to gross-motor tasks for different training parameters. Ten healthy participants were recruited to conduct right elbow extension-flexion in three robot-assisted modes. The Passive mode was fully motor-actuated, while Active1 and Active2 modes involved active engagement with different resistance levels. FNIRS data of three identical runs were used to assess the within-session reliability in terms of the map- ( R2 ) and cluster-wise ( Roverlap ) spatial reproducibility and the intraclass correlation (ICC) of temporal features. The results revealed good spatial reliability ( R2 up to 0.69, Roverlap up to 0.68) at the subject level. Besides, the within-session temporal reliabilities of Slope, Max/Min, and Mean were between good and excellent ( ICC < 0.86). We also found that the within-session reliability was positively correlated with the intensity of the training mode, except for the temporal reliability of HbO in Active2 mode. Overall, our results demonstrated good within-session reliability of fNIRS responses, suggesting fNIRS as reliable neurofeedback for constructing closed-loop robot-assisted rehabilitation systems.

A New Compound-Limbs Paradigm: Integrating Upper-Limb Swing Improves Lower-Limb Stepping Intention Decoding From EEG.

Brain-computer interface (BCI) systems based on spontaneous electroencephalography (EEG) hold the promise to implement human voluntary control of lower-extremity powered exoskeletons. However, current EEG-BCI paradigms do not consider the cooperation of upper and lower limbs during walking, which is inconsistent with natural human stepping patterns. To deal with this problem, this study proposed a stepping-matched human EEG-BCI paradigm that involved actions of both unilateral lower and contralateral upper limbs (also referred to as compound-limbs movement). Experiments were conducted in motor execution (ME) and motor imagery (MI) conditions to validate the feasibility. Common spatial pattern (CSP) proposed subject-specific CSP (SSCSP), and filter-bank CSP (FBCSP) methods were applied for feature extraction, respectively. The best average classification results based on SSCSP indicated that the accuracies of compound-limbs paradigms in ME and MI conditions achieved 89.02% ± 12.84% and 73.70% ± 12.47%, respectively. Although they were 2.03% and 5.68% lower than those of the single-upper-limb mode that does not match human stepping patterns, they were 24.30% and 11.02% higher than those of the single-lower-limb mode. These findings indicated that the proposed compound-limbs EEG-BCI paradigm is feasible for decoding human stepping intention and thus provides a potential way for natural human control of walking assistance devices.

Decoding Coordinated Directions of Bimanual Movements From EEG Signals.

Bimanual coordination is common in human daily life, whereas current research focused mainly on decoding unimanual movement from electroencephalogram (EEG) signals. Here we developed a brain-computer interface (BCI) paradigm of task-oriented bimanual movements to decode coordinated directions from movement-related cortical potentials (MRCPs) of EEG. Eight healthy subjects participated in the target-reaching task, including (1) performing leftward, midward, and rightward bimanual movements, and (2) performing leftward and rightward unimanual movements. A combined deep learning model of convolution neural network and bidirectional long short-term memory network was proposed to classify movement directions from EEG. Results showed that the average peak classification accuracy for three coordinated directions of bimanual movements reached 73.39 ± 6.35 %. The binary classification accuracies achieved 80.24 ± 6.25 , 82.62 ± 7.82 , and 86.28 ± 5.50 % for leftward versus midward, rightward versus midward and leftward versus rightward, respectively. We also compared the binary classification (leftward versus rightward) of bimanual, left-hand, and right-hand movements, and accuracies achieved 86.28 ± 5.50 %, 75.67 ± 7.18 %, and 77.79 ± 5.65 %, respectively. The results indicated the feasibility of decoding human coordinated directions of task-oriented bimanual movements from EEG.

Continuous Bimanual Trajectory Decoding of Coordinated Movement From EEG Signals.

While many voluntary movements involve bimanual coordination, few attempts have been made to simultaneously decode the trajectory of bimanual movements from electroencephalogram (EEG) signals. In this study, we proposed a novel bimanual brain-computer interface (BCI) paradigm to reconstruct the continuous trajectory of both hands during coordinated movements from EEG. The protocol required human subjects to complete a bimanual reaching task to the left, middle, or right target while EEG data were collected. A multi-task deep learning model combining the EEGNet and long short-term memory network (LSTM) was proposed to decode bimanual trajectories, including position and velocity. Decoding performance was evaluated in terms of the correlation coefficient (CC) and normalized root mean square error (NRMSE) between decoded and real trajectories. Experimental results from 13 human subjects showed that the grand-averaged combined CC values achieved 0.54 and 0.42 for position and velocity decoding, respectively. The corresponding combined NRMSE values were 0.22 and 0.23. Both CC and NRMSE were significantly superior to the chance level (p<0.05). Comparative experiments also indicated that the proposed model significantly outperformed some other commonly-used methods in terms of CC and NRMSE for continuous trajectory decoding. These findings demonstrated the feasibility of simultaneously decoding bimanual trajectory from EEG, indicating the potential of bimanual control for coordinated tasks.

Characterization of Bimanual Cyclical Tasks From Single-Trial EEG-fNIRS Measurements.

Robot-assisted bimanual training is promising to improve motor function and cortical reorganization for hemiparetic stroke patients. Closing the rehabilitation training loop with neurofeedback can help refine training protocols in time for better engagements and outcomes. However, due to the low signal-to-noise ratio (SNR) and non-stationary properties of neural signals, reliable characterization of bimanual training-induced neural activities from single-trial measurement is challenging. In this study, ten human participants were recruited conducting robot-assisted bimanual cyclical tasks (in-phase, 90° out-of-phase, and anti-phase) when concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were recorded. A unified EEG-fNIRS bimodal signal processing framework was proposed to characterize neural activities induced by three types of bimanual cyclical tasks. In this framework, novel artifact removal methods were used to improve the SNR and the task-related component analysis (TRCA) was introduced to increase the reproducibility of EEG-fNIRS bimodal features. The optimized features were transformed into low-dimensional indicators to reliably characterize bimanual training-induced neural activation. The SVM classification results of three bimanual cyclical tasks revealed a good discrimination ability of EEG-fNIRS bimodal indicators (90.1%), which was higher than that using EEG (74.8%) or fNIRS (82.2%) alone, supporting the proposed method as a feasible technique to characterize neural activities during robot-assisted bimanual training.

Ginsenoside Re Attenuates Isoproterenol-Induced Myocardial Injury in Rats.

Objective. Panax ginseng is widely used for treatment of cardiovascular disorders in China. Ginsenoside Re is the main chemical component of Panax ginseng. This study aimed to investigate the protective effect of Ginsenoside Re on isoproterenol-induced myocardial injury in rats. Methods. Male Wistar rats were orally given Ginsenoside Re (5, 20 mg/kg) daily for 7 days. Isoproterenol was subcutaneously injected into the rats for two consecutive days at a dosage of 20 mg/kg/day (on 6th and 7th day). Six hours after the last isoproterenol injection, troponin T level and creatine kinase-MB (CK-MB) activity were assayed. Histopathological examination of heart tissues was performed. The levels of malondialdehyde (MDA) and glutathione (GSH) in heart tissues were measured. The nuclear factor erythroid 2-related factor 2 (Nrf2) content in nucleus and the proteins of glutathione cysteine ligase catalytic subunit (GCLC) and glutathione cysteine ligase modulatory subunit (GCLM) in heart tissues were assayed by western blotting method. Results. Treatment with Ginsenoside Re at dose of 5, 20 mg/kg reduced troponin T level and CK-MB activity of rats subjected to isoproterenol. The cardioprotective effect of Ginsenoside Re was further confirmed by histopathological examination which showed that Ginsenoside Re attenuated the necrosis and inflammatory cells infiltration. Ginsenoside Re inhibited the increase of MDA content and the decrease of GSH in heart tissues. Moreover, the Nrf2 content in nucleus and the expressions of GCLC and GCLM were significantly increased in the animals treated with Ginsenoside Re. Conclusion. These findings suggested that Ginsenoside Re possesses the property to attenuate isoproterenol-induced myocardial ischemic injury by regulating the antioxidation function in cardiomyocytes.

Ginsenoside Rg3 Improves Cardiac Function after Myocardial Ischemia/Reperfusion via Attenuating Apoptosis and Inflammation.

Objectives. Ginsenoside Rg3 is one of the ginsenosides which are the main constituents isolated from Panax ginseng. Previous study demonstrated that ginsenoside Rg3 had a protective effect against myocardial ischemia/reperfusion- (I/R-) induced injury. Objective. This study was designed to evaluate the effect of ginsenoside Rg3 on cardiac function impairment induced by myocardial I/R in rats. Methods. Sprague-Dawley rats were subjected to myocardial I/R. Echocardiographic and hemodynamic parameters and histopathological examination were carried out. The expressions of P53, Bcl-2, Bax, and cleaved caspase-3 and the levels of TNF-α and IL-1ß in the left ventricles were measured. Results. Ginsenoside Rg3 increased a left ventricular fractional shortening and left ventricular ejection fraction. Treatment with ginsenoside Rg3 also alleviated increases of left ventricular end diastolic pressure and decreases of left ventricular systolic pressure and ±dp/dt in myocardial I/R-rats. Ginsenoside Rg3 decreased apoptosis cells through inhibiting the activation of caspase-3. Ginsenoside Rg3 also caused significant reductions of the contents of TNF-α and IL-1ß in left ventricles of myocardial I/R-rats. Conclusion. The findings suggested that ginsenoside Rg3 possessed the effect of improving myocardial I/R-induced cardiac function impairment and that the mechanism of pharmacological action of ginsenoside Rg3 was related to its properties of antiapoptosis and anti-inflammation.