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https://www.selleckchem.com/EGFR(HER).html Neurofeedback training (NFT) is a non-invasive, safe, and effective method of regulating the nerve state of the brain. Presently, NFT is widely used to prevent and rehabilitate brain diseases and improve an individual's external performance. Among the various NFT methods, NFT to improve sport performance (SP-NFT) has become an important research and application focus worldwide. Several studies have shown that the method is effective in improving brain function and motor control performance. However, appropriate reviews and prospective directions for this technology are lacking. This paper proposes an SP-NFT classification method based on user experience, classifies and discusses various SP-NFT research schemes reported in the existing literature, and reviews the technical principles, application scenarios, and usage characteristics of different SP-NFT schemes. Several key issues in SP-NFT development, including the factors involved in neural mechanisms, scheme selection, learning basis, and experimental implementation, are discussed. Finally, directions for the future development of SP-NFT, including SP-NFT based on other electroencephalograph characteristics, SP-NFT integrated with other technologies, and SP-NFT commercialization, are suggested. These discussions are expected to provide some valuable ideas to researchers in related fields.After 35 rounds of talks over the past seven years, the negotiations on the China-EU Comprehensive Agreement on Investment (CAI) passed the finishing line at the end of 2020, a timely gift for the 45th anniversary of the establishment of China-EU diplomatic ties. As a most comprehensive and significant economic and trade agreement between China and the EU, CAI marks a highly relevant step to meet the expectations of different sectors and should be cherished by both sides. In this paper, the transfer learning method has been implemented to chest X-ray (CXR) and computed tomography (CT) bio

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