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

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

Compressed glassy carbon: An ultrastrong and elastic interpenetrating graphene network

Carbon’s unique ability to have both sp2 and sp3 bonding states gives rise to a range of physical attributes, including excellent mechanical and electrical properties. We show that a series of lightweight, ultrastrong, hard, elastic, and conductive carbons are recovered after compressing sp2-hybridized glassy carbon at various temperatures. Compression induces the local buckling of graphene sheets through sp3 nodes to form interpenetrating graphene networks with long-range disorder and short-range order on the nanometer scale. The compressed glassy carbons have extraordinary specific compressive strengths—more than two times that of commonly used ceramics—and simultaneously exhibit robust elastic recovery in response to local deformations. This type of carbon is an optimal ultralight, ultrastrong material for a wide range of multifunctional applications, and the synthesis methodology demonstrates potential to access entirely new metastable materials with exceptional properties

System engineering and key technologies research and practice of smart mine

In order to overcome the problems existed in the construction process of complex giant system of smart mine, the overall technical framework of smart mine construction was proposed, and a detailed description was made from the aspects of smart mine system model construction, underground space reconstruction and model dynamic update, machine vision measurement technology, radio frequency explosion-proof test method of gas environment, mine safety closed-loop control system, smart mine standard system, etc. The smart mine was divided into information sensing support layer, edge computing layer, cloud data center, multi-type network, intelligent mine production management and control platform, intelligent mine production system and intelligent mine operation and maintenance management system. The smart mine technology architecture based on the deep integration of multiple systems was built. Based on the knowledge map and information extraction of smart mine, a smart mine system model driven by data innovation, based on communication network and centered on data computing power was constructed. A 3D visual space model based on machine vision perception information and supplemented by other perception information was constructed, and a 3D vision and spatial reconstruction framework of mine underground scene was proposed to realize the reconstruction and dynamic update of the 3D space of underground mine. Based on machine vision technology, the identification algorithm of downhole equipment position and coal-rock interface was developed to realize the simultaneous aerial measurement of fully mechanized mining equipment group and coal-rock interface. The limitations and shortcomings of the current explosion-proof standards on the power of 5G base stations in the underground gas environment were discussed, and a special test device for radio-frequency electromagnetic energy explosion-proof was designed and developed, which provides a method for reference to improve the power threshold of underground 5G base stations. The technical framework of mine disaster closed-loop management and control system, which integrates comprehensive perception of disaster information, independent decision-making of prevention and control plan, and coordinated control of prevention and control equipment, was proposed to realize advanced prediction and early warning and coordinated prevention and control of underground disasters. The framework of intelligent coal mine standard system was constructed, the typical cases of intelligent construction of coal mine and metal mine in China were analyzed, and the development trend and suggestions of intelligent mine construction were put forward

Carbon Reduction Effect of Green Technology Innovation from the Perspective of Energy Consumption and Efficiency

Consumption-oriented or efficiency-oriented, it is a hard choice for the green technology innovation pathway. This paper uses the intermediary model to empirically analyze the panel data from 250 prefecture-level cities in China from 2010 to 2019. The conclusions show that: 1. At present, energy consumption-oriented green technology innovation at the national level in China shows a completely intermediary effect, which has a more obvious emission reduction effect; compared with energy consumption, energy efficiency-oriented green technology innovation only has a very weak intermediary effect of 6.58%. 2. Only the Eastern non-resource cities and the Midwest resource cities’ green technology innovation have the effect of energy efficiency-oriented emission reduction, accounting for 8.11% and 9.02%, respectively. 3. Both the Eastern resource cities and the Midwest non-resource cities have no intermediary effect on energy efficiency, so carbon emission reduction is more difficult than in other cities

Carbon Reduction Effect of Green Technology Innovation from the Perspective of Energy Consumption and Efficiency

Consumption-oriented or efficiency-oriented, it is a hard choice for the green technology innovation pathway. This paper uses the intermediary model to empirically analyze the panel data from 250 prefecture-level cities in China from 2010 to 2019. The conclusions show that: 1. At present, energy consumption-oriented green technology innovation at the national level in China shows a completely intermediary effect, which has a more obvious emission reduction effect; compared with energy consumption, energy efficiency-oriented green technology innovation only has a very weak intermediary effect of 6.58%. 2. Only the Eastern non-resource cities and the Midwest resource cities’ green technology innovation have the effect of energy efficiency-oriented emission reduction, accounting for 8.11% and 9.02%, respectively. 3. Both the Eastern resource cities and the Midwest non-resource cities have no intermediary effect on energy efficiency, so carbon emission reduction is more difficult than in other cities

Dietary Phospholipids Alleviate Diet-Induced Obesity in Mice: Which Fatty Acids and Which Polar Head

The weight loss effects of dietary phospholipids have been extensively studied. However, little attention has been paid to the influence of phospholipids (PLs) with different fatty acids and polar headgroups on the development of obesity. High-fat-diet-fed mice were administrated with different kinds of PLs (2%, w/w) with specific fatty acids and headgroups, including EPA-enriched phosphatidylcholine/phosphatidylethanolamine/phosphatidylserine (EPA-PC/PE/PS), DHA-PC/PE/PS, Egg-PC/PE/PS, and Soy-PC/PE/PS for eight weeks. Body weight, white adipose tissue weight, and the levels of serum lipid and inflammatory markers were measured. The expression of genes related to lipid metabolism in the liver were determined. The results showed that PLs decreased body weight, fat storage, and circulating lipid levels, and EPA-PLs had the best efficiency. Serum TNF-α, MCP-1 levels were significantly reduced via treatment with DHA-PLs and PS groups. Mechanistic investigation revealed that PLs, especially EPA-PLs and PSs, reduced fat accumulation through enhancing the expression of genes involved in fatty acid β-oxidation (Cpt1a, Cpt2, Cd36, and Acaa1a) and downregulating lipogenesis gene (Srebp1c, Scd1, Fas, and Acc) expression. These data suggest that EPA-PS exhibits the best effects among other PLs in terms of ameliorating obesity, which might be attributed to the fatty acid composition of phospholipids, as well as their headgroup

The accuracy of intraocular lens power calculation formulas based on artificial intelligence in highly myopic eyes: a systematic review and network meta-analysis

ObjectiveTo systematically compare and rank the accuracy of AI-based intraocular lens (IOL) power calculation formulas and traditional IOL formulas in highly myopic eyes.MethodsWe screened PubMed, Web of Science, Embase, and Cochrane Library databases for studies published from inception to April 2023. The following outcome data were collected: mean absolute error (MAE), percentage of eyes with a refractive prediction error (PE) within ±0.25, ±0.50, and ±1.00 diopters (D), and median absolute error (MedAE). The network meta-analysis was conducted by R 4.3.0 and STATA 17.0.ResultsTwelve studies involving 2,430 adult myopic eyes (with axial lengths >26.0 mm) that underwent uncomplicated cataract surgery with mono-focal IOL implantation were included. The network meta-analysis of 21 formulas showed that the top three AI-based formulas, as per the surface under the cumulative ranking curve (SUCRA) values, were XGBoost, Hill-RBF, and Kane. The three formulas had the lowest MedAE and were more accurate than traditional vergence formulas, such as SRK/T, Holladay 1, Holladay 2, Haigis, and Hoffer Q regarding MAE, percentage of eyes with PE within ±0.25, ±0.50, and ±1.00 D.ConclusionsThe top AI-based formulas for calculating IOL power in highly myopic eyes were XGBoost, Hill-RBF, and Kane. They were significantly more accurate than traditional vergence formulas and ranked better than formulas with Wang–Koch AL modifications or newer generations of formulas such as Barrett and Olsen.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022335969