Enhancing Motor Imagery Decoding in Brain Computer Interfaces using Riemann Tangent Space Mapping and Cross Frequency Coupling

Objective: Motor Imagery (MI) serves as a crucial experimental paradigm within the realm of Brain Computer Interfaces (BCIs), aiming to decoding motor intentions from electroencephalogram (EEG) signals. Method: Drawing inspiration from Riemannian geometry and Cross-Frequency Coupling (CFC), this paper introduces a novel approach termed Riemann Tangent Space Mapping using Dichotomous Filter Bank with Convolutional Neural Network (DFBRTS) to enhance the representation quality and decoding capability pertaining to MI features. DFBRTS first initiates the process by meticulously filtering EEG signals through a Dichotomous Filter Bank, structured in the fashion of a complete binary tree. Subsequently, it employs Riemann Tangent Space Mapping to extract salient EEG signal features within each sub-band. Finally, a lightweight convolutional neural network is employed for further feature extraction and classification, operating under the joint supervision of cross-entropy and center loss. To validate the efficacy, extensive experiments were conducted using DFBRTS on two well-established benchmark datasets: the BCI competition IV 2a (BCIC-IV-2a) dataset and the OpenBMI dataset. The performance of DFBRTS was benchmarked against several state-of-the-art MI decoding methods, alongside other Riemannian geometry-based MI decoding approaches. Results: DFBRTS significantly outperforms other MI decoding algorithms on both datasets, achieving a remarkable classification accuracy of 78.16% for four-class and 71.58% for two-class hold-out classification, as compared to the existing benchmarks.Comment: 22 pages, 7 figure

Improved Motor Imagery Classification Using Adaptive Spatial Filters Based on Particle Swarm Optimization Algorithm

As a typical self-paced brain-computer interface (BCI) system, the motor imagery (MI) BCI has been widely applied in fields such as robot control, stroke rehabilitation, and assistance for patients with stroke or spinal cord injury. Many studies have focused on the traditional spatial filters obtained through the common spatial pattern (CSP) method. However, the CSP method can only obtain fixed spatial filters for specific input signals. Besides, CSP method only focuses on the variance difference of two types of electroencephalogram (EEG) signals, so the decoding ability of EEG signals is limited. To obtain more effective spatial filters for better extraction of spatial features that can improve classification to MI-EEG, this paper proposes an adaptive spatial filter solving method based on particle swarm optimization algorithm (PSO). A training and testing framework based on filter bank and spatial filters (FBCSP-ASP) is designed for MI EEG signal classification. Comparative experiments are conducted on two public datasets (2a and 2b) from BCI competition IV, which show the outstanding average recognition accuracy of FBCSP-ASP. The proposed method has achieved significant performance improvement on MI-BCI. The classification accuracy of the proposed method has reached 74.61% and 81.19% on datasets 2a and 2b, respectively. Compared with the baseline algorithm (FBCSP), the proposed algorithm improves 11.44% and 7.11% on two datasets respectively. Furthermore, the analysis based on mutual information, t-SNE and Shapley values further proves that ASP features have excellent decoding ability for MI-EEG signals, and explains the improvement of classification performance by the introduction of ASP features.Comment: 25 pages, 8 figure

Effect of Yugan Sanjie decoction on expressions of regulatory T cells, serum P21 protein and vascular endothelial growth factor in mice with hepatocellular carcinoma

Purpose: To investigate the effect of Yugan Sanjie decoction on the expressions of regulatory T cells (Tregs), serum P21 protein and vascular endothelial growth factor (VEGF) in mice with hepatocellular carcinoma. Methods: A total of forty specific-pathogen-free (SPF) Kunming mice were randomly assigned to four groups of 10 mice each. Except for normal control group, the other three groups were transfected with hepatoma-22 (H22) cells to establish a mouse model of liver cancer. Mice in the cyclophosphamide group was given cyclophosphamide at a dose of 20 mg/kg daily intragastrically, while those in decoction group were treated with Yugan Sanjie decoction (0.4 ml/kg/day) intraperitoneally. After 30 days of treatment, serum levels of CD4+ Th17, CD4+CD25+ Treg, Th17/Treg, TNF-α, and VEGF were determined. Results: There was lower serum level of CD4+ Th17 in the decoction group than in negative control and cyclophosphamide groups (p < 0.05). However, higher serum levels of CD4+CD25+ Treg and Th17/Treg were seen in the decoction group, relative to the negative control and cyclophosphamide groups (p < 0.05). Serum TNF-α was also markedly elevated in decoction group, when compared with negative control and cyclophosphamide groups (p < 0.05). Serum VEGF was markedly lower in decoction group than in negative control and cyclophosphamide groups, and was appreciably lower in cyclophosphamide group than in negative control group (p < 0.05). Conclusion: Yugan Sanjie decoction effectively alleviates clinical symptoms of LC, and improves immune function of mice by regulating serum levels of T lymphocytes. These findings provide scientific support for a new treatment strategy

Neural Networks and Learning Systems for Human Machine Interfacing

With developments of the sensor and computing technologies, human-machine interfaces (HMIs) are designed to meet the increasing user demands of machines and systems. This is because human effects are becoming the key issues to allow some advanced mechanical devices, such as robots and biometric systems, to perform complicate tasks intelligently in an unknown environment. An effective HMI with learning ability can process, interpret, recognize, and simulate the intention and behaviors of human beings, and then utilize intelligent algorithms to drive the machine devices. The HMIs also enable us to bring humanistic intelligence and actions in robotic devices, biometric systems and other machines through two-ways interactions, such as using deep neural networks. In recent years, a growing number of researchers and studies focusing on this area have clearly demonstrated the importance of learning systems for HMIs

Multicentennial to millennial–scale changes in the East Asian summer monsoon during Greenland interstadial 25

A multidecadal-resolved stalagmite δ18O record from two nearby caves, Lianhua and Dragon, in Shanxi Province, northern China, characterizes the detailed East Asian summer monsoon (EASM) intensity changes at 114.6–108.3 ka during Marine Oxygen Isotope Stage 5d. Our record shows an intensification of the EASM at 114.6–109.5 ka, followed by a rapid weakening at 109.5–108.4 ka. The millennial-scale strong monsoonal event appears to be correlated with the warm Greenland interstadial 25 (GI 25), whereas the weak monsoonal event is related to the cold Greenland stadial 25 within dating errors. The GI 25 monsoonal event registered in our record is also documented in various published time series from different regions of China. The lines of evidence indicate that this event occurred over the entirety of monsoonal China and was also broadly antiphase, similar to the corresponding event on a millennial time scale in the South American monsoon territory. In our record, one 700 yr weak monsoon event at 110.7+0.6−0.5 to 110.0+0.8−0.4 ka divides the GI 25 into three substages. These multicentennial to millennial–scale monsoon events correspond to two warm periods and an intervening cold interval for the intra-interstadial climate oscillations within GI 25, thus supporting a persistent coupling of the high- and low-latitude climate systems over the last glacial period

Space advanced technology demonstration satellite

The Space Advanced Technology demonstration satellite (SATech-01), a mission for low-cost space science and new technology experiments, organized by Chinese Academy of Sciences (CAS), was successfully launched into a Sun-synchronous orbit at an altitude of similar to 500 km on July 27, 2022, from the Jiuquan Satellite Launch Centre. Serving as an experimental platform for space science exploration and the demonstration of advanced common technologies in orbit, SATech-01 is equipped with 16 experimental payloads, including the solar upper transition region imager (SUTRI), the lobster eye imager for astronomy (LEIA), the high energy burst searcher (HEBS), and a High Precision Magnetic Field Measurement System based on a CPT Magnetometer (CPT). It also incorporates an imager with freeform optics, an integrated thermal imaging sensor, and a multi-functional integrated imager, etc. This paper provides an overview of SATech-01, including a technical description of the satellite and its scientific payloads, along with their on-orbit performance

Omecamtiv mecarbil in chronic heart failure with reduced ejection fraction, GALACTIC‐HF: baseline characteristics and comparison with contemporary clinical trials

Aims: The safety and efficacy of the novel selective cardiac myosin activator, omecamtiv mecarbil, in patients with heart failure with reduced ejection fraction (HFrEF) is tested in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTIC‐HF) trial. Here we describe the baseline characteristics of participants in GALACTIC‐HF and how these compare with other contemporary trials. Methods and Results: Adults with established HFrEF, New York Heart Association functional class (NYHA) ≥ II, EF ≤35%, elevated natriuretic peptides and either current hospitalization for HF or history of hospitalization/ emergency department visit for HF within a year were randomized to either placebo or omecamtiv mecarbil (pharmacokinetic‐guided dosing: 25, 37.5 or 50 mg bid). 8256 patients [male (79%), non‐white (22%), mean age 65 years] were enrolled with a mean EF 27%, ischemic etiology in 54%, NYHA II 53% and III/IV 47%, and median NT‐proBNP 1971 pg/mL. HF therapies at baseline were among the most effectively employed in contemporary HF trials. GALACTIC‐HF randomized patients representative of recent HF registries and trials with substantial numbers of patients also having characteristics understudied in previous trials including more from North America (n = 1386), enrolled as inpatients (n = 2084), systolic blood pressure < 100 mmHg (n = 1127), estimated glomerular filtration rate < 30 mL/min/1.73 m2 (n = 528), and treated with sacubitril‐valsartan at baseline (n = 1594). Conclusions: GALACTIC‐HF enrolled a well‐treated, high‐risk population from both inpatient and outpatient settings, which will provide a definitive evaluation of the efficacy and safety of this novel therapy, as well as informing its potential future implementation

Casual Association Between Coffee Intake and Prostate Cancer Based on Two-sample Mendel Randomization

Objective To assess the causal relationship between coffee intake and prostate cancer risk by using the two-sample Mendel randomization (MR) method. Methods The genome-wide association study (GWAS) data on coffee intake (exposure) and prostate cancer (outcome) were obtained from two independent data sets in UK Biobank. The inverse variance weighted method (IVW), weighted median estimator method (WME), and MR-Egger method were used for MR analyses. The OR value and 95%CI were used to represent the association between coffee intake and prostate cancer. In addition, the MR-Egger method was performed for pleiotropic and heterogeneity tests, and the leave-one-out method was used for sensitivity analysis. Results A total of 38 SNP were selected as instrumental variables. The IVW method showed that coffee intake might reduce the risk of prostate cancer (OR=0.994; 95%CI: 0.990-0.999; P=0.009). The WME method obtained the same conclusions (OR=0.991; 95%CI: 0.985-0.999; P=0.018), but MR-Egger regression did not find a causal relationship between coffee intake and prostate cancer (OR=0.992; 95%CI: 0.983-1.000; P=0.084). The MR-Egger method showed no pleiotropy (intercept=4.2E-5; P=0.581) or heterogeneity (Q=27.20; P=0.854) among the instrumental variables. The sensitivity analysis indicated that the conclusion was robust. Conclusion Two-sample Mendel randomization analysis reveals that coffee consumption might reduce the risk of prostate cancer