Comparison Between Movement-Based and Task-Based Mirror Therapies on Improving Upper Limb Functions in Patients With Stroke: A Pilot Randomized Controlled Trial

Objective: The aim of this trial was to compare the effect of movement-based mirror therapy (MMT) and task-based mirror therapy (TMT) on improving upper limb functions in patients with stroke.Methods: A total of 34 patients with sub-acute stroke with mildly to moderately impaired upper limb motor functions. The participants were randomly allocated to one of three groups: MMT, TMT, and conventional treatment (CT). The MMT group underwent movement-based mirror therapy for around 30 min/day, 5 days/week, for 4 weeks, whereas the TMT group underwent dose-matched TMT. The CT group underwent only conventional rehabilitation. The MMT and TMT groups underwent CT in addition to their mirror therapy. Blinded assessments were administered at baseline and immediately after the intervention. Upper limb motor functions, measured using Fugl-Meyer Assessment-upper extremity (FMA-UE), Wolf Motor Function Test (WMFT), and hand grip strength; upper limb spasticity, measured using the modified Ashworth scale (MAS); and activities of daily living, measured using the modified Barthel index (MBI).Results: A significant time-by-group interaction effect was noted in FMA-UE. Post-hoc analysis of change scores showed that MMT yielded a better effect on improving FMA-UE than the other two therapies, at a marginally significant level (P = 0.050 and 0.022, respectively). No significant interaction effect was noted in WMFT, hand grip strength, MAS, and MBI.Conclusion: Both MMT and TMT are effective in improving the upper limb function of patients with mild to moderate hemiplegia due to stroke. Nevertheless, MMT seems to be superior to TMT in improving hemiplegic upper extremity impairment. Further studies with larger stroke cohorts are expected to be inspired by this pilot trial.Trial registration number: No. ChiCTR1800019043 (http://www.chictr.org.cn/index.aspx

A sequential learning model with GNN for EEG-EMG-based stroke rehabilitation BCI

IntroductionBrain-computer interfaces (BCIs) have the potential in providing neurofeedback for stroke patients to improve motor rehabilitation. However, current BCIs often only detect general motor intentions and lack the precise information needed for complex movement execution, mainly due to insufficient movement execution features in EEG signals.MethodsThis paper presents a sequential learning model incorporating a Graph Isomorphic Network (GIN) that processes a sequence of graph-structured data derived from EEG and EMG signals. Movement data are divided into sub-actions and predicted separately by the model, generating a sequential motor encoding that reflects the sequential features of the movements. Through time-based ensemble learning, the proposed method achieves more accurate prediction results and execution quality scores for each movement.ResultsA classification accuracy of 88.89% is achieved on an EEG-EMG synchronized dataset for push and pull movements, significantly outperforming the benchmark method's performance of 73.23%.DiscussionThis approach can be used to develop a hybrid EEG-EMG brain-computer interface to provide patients with more accurate neural feedback to aid their recovery

Subject-independent EEG classification based on a hybrid neural network

A brain-computer interface (BCI) based on the electroencephalograph (EEG) signal is a novel technology that provides a direct pathway between human brain and outside world. For a traditional subject-dependent BCI system, a calibration procedure is required to collect sufficient data to build a subject-specific adaptation model, which can be a huge challenge for stroke patients. In contrast, subject-independent BCI which can shorten or even eliminate the pre-calibration is more time-saving and meets the requirements of new users for quick access to the BCI. In this paper, we design a novel fusion neural network EEG classification framework that uses a specially designed generative adversarial network (GAN), called a filter bank GAN (FBGAN), to acquire high-quality EEG data for augmentation and a proposed discriminative feature network for motor imagery (MI) task recognition. Specifically, multiple sub-bands of MI EEG are first filtered using a filter bank approach, then sparse common spatial pattern (CSP) features are extracted from multiple bands of filtered EEG data, which constrains the GAN to maintain more spatial features of the EEG signal, and finally we design a convolutional recurrent network classification method with discriminative features (CRNN-DF) to recognize MI tasks based on the idea of feature enhancement. The hybrid neural network proposed in this study achieves an average classification accuracy of 72.74 ± 10.44% (mean ± std) in four-class tasks of BCI IV-2a, which is 4.77% higher than the state-of-the-art subject-independent classification method. A promising approach is provided to facilitate the practical application of BCI

Considerable effects of lateralization and aging in intracortical excitation and inhibition

IntroductionFindings based on the use of transcranial magnetic stimulation and electromyography (TMS-EMG) to determine the effects of motor lateralization and aging on intracortical excitation and inhibition in the primary motor cortex (M1) are inconsistent in the literature. TMS and electroencephalography (TMS-EEG) measures the excitability of excitatory and inhibitory circuits in the brain cortex without contamination from the spine and muscles. This study aimed to investigate the effects of motor lateralization (dominant and non-dominant hemispheres) and aging (young and older) and their interaction effects on intracortical excitation and inhibition within the M1 in healthy adults, measured using TMS-EMG and TMS-EEG.MethodsThis study included 21 young (mean age = 28.1 ± 3.2 years) and 21 older healthy adults (mean age = 62.8 ± 4.2 years). A battery of TMS-EMG measurements and single-pulse TMS-EEG were recorded for the bilateral M1.ResultsTwo-way repeated-measures analysis of variance was used to investigate lateralization and aging and the lateralization-by-aging interaction effect on neurophysiological outcomes. The non-dominant M1 presented a longer cortical silent period and larger amplitudes of P60, N100, and P180. Corticospinal excitability in older participants was significantly reduced, as supported by a larger resting motor threshold and lower motor-evoked potential amplitudes. N100 amplitudes were significantly reduced in older participants, and the N100 and P180 latencies were significantly later than those in young participants. There was no significant lateralization-by-aging interaction effect in any outcome.ConclusionLateralization and aging have independent and significant effects on intracortical excitation and inhibition in healthy adults. The functional decline of excitatory and inhibitory circuits in the M1 is associated with aging

Intracortical and intercortical networks in patients after stroke: a concurrent TMS-EEG study

Abstract Background Concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) recording provides information on both intracortical reorganization and networking, and that information could yield new insights into post-stroke neuroplasticity. However, a comprehensive investigation using both concurrent TMS-EEG and motor-evoked potential-based outcomes has not been carried out in patients with chronic stroke. Therefore, this study sought to investigate the intracortical and network neurophysiological features of patients with chronic stroke, using concurrent TMS-EEG and motor-evoked potential-based outcomes. Methods A battery of motor-evoked potential-based measures and concurrent TMS-EEG recording were performed in 23 patients with chronic stroke and 21 age-matched healthy controls. Results The ipsilesional primary motor cortex (M1) of the patients with stroke showed significantly higher resting motor threshold (P = 0.002), reduced active motor-evoked potential amplitudes (P = 0.001) and a prolonged cortical silent period (P = 0.007), compared with their contralesional M1. The ipsilesional stimulation also produced a reduction in N100 amplitude of TMS-evoked potentials around the stimulated M1 (P = 0.007), which was significantly correlated with the ipsilesional resting motor threshold (P = 0.011) and motor-evoked potential amplitudes (P = 0.020). In addition, TMS-related oscillatory power was significantly reduced over the ipsilesional midline-prefrontal and parietal regions. Both intra/interhemispheric connectivity and network measures in the theta band were significantly reduced in the ipsilesional hemisphere compared with those in the contralesional hemisphere. Conclusions The ipsilesional M1 demonstrated impaired GABA-B receptor-mediated intracortical inhibition characterized by reduced duration, but reduced magnitude. The N100 of TMS-evoked potentials appears to be a useful biomarker of post-stroke recovery

Synergistically Enhanced Interfacial Interaction to Polysulfide via N,O Dual-Doped Highly Porous Carbon Microrods for Advanced Lithium-Sulfur Batteries

Lithium-sulfur (Li-S) batteries have received tremendous attention because of their extremely high theoretical capacity (1672 mA h g -1 ) and energy density (2600 W h kg -1 ). Nevertheless, the commercialization of Li-S batteries has been blocked by the shuttle effect of lithium polysulfide intermediates, the insulating nature of sulfur, and the volume expansion during cycling. Here, hierarchical porous N,O dual-doped carbon microrods (NOCMs) were developed as sulfur host materials with a large pore volume (1.5 cm 3 g -1 ) and a high surface area (1147 m 2 g -1 ). The highly porous structure of the NOCMs can act as a physical barrier to lithium polysulfides, while N and O functional groups enhance the interfacial interaction to trap lithium polysulfides, permitting a high loading amount of sulfur (79-90 wt % in the composite). Benefiting from the physical and chemical anchoring effect to prevent shuttling of polysulfides, S@NOCMs composites successfully solve the problems of low sulfur utilization and fast capacity fade and exhibit a stable reversible capacity of 1071 mA h g -1 after 160 cycles with nearly 100% Coulombic efficiency at 0.2 C. The N,O dual doping treatment to porous carbon microrods paves a way toward rational design of high-performance Li-S cathodes with high energy density

High-performance room-temperature sodium-sulfur battery enabled by electrocatalytic sodium polysulfides full conversion

© 2020 The Royal Society of Chemistry. Room-temperature sodium-sulfur (RT-Na-S) batteries are highly desirable for grid-scale stationary energy storage due to their low cost; however, short cycling stability caused by the incomplete conversion of sodium polysulfides is a major issue for their application. Herein, we introduce an effective sulfiphilic host, gold nanodots decorated on hierarchical N-doped carbon microspheres (CN/Au/S), to achieve completely reversible conversion reactions in the S cathode by electrocatalyzing the low-kinetics conversion of Na2S4 into NaS2 (discharge process) or S (charge process). Besides, gold nanodots and N-doped carbon can increase the conductivity of the S cathode and provide strong polar-polar adsorption of sodium polysulfides to alleviate the shuttling effects. When serving as the cathode, the CN/Au/S composite can realize enhanced sulfur utilization, excellent cycling stability, and outstanding rate capability. This work deepens our understanding of the catalytic effect of gold atoms on sulfur molecules, opening a new avenue for cathode design and development of advanced RT-Na-S batteries

Overview of transmission expansion planning in the market environment

The reform of the electricity market has brought many new challenges to transmission expansion planning (TEP). This paper categorizes existing transmission planning literatures in the market environment, summarizes popular research issues arising from this field in recent years, analyzes model structures based on the objectives from the power transmission side and generators’ behavior, explores the practicality of models and methods in real power systems from the perspective of load and the scale of test systems, summarizes characteristics of the existing literature, analyzes deficiencies in current works, and provides some suggestions for future research