Canbiao Wu, Nayu Chen, Tuo Sun, Ping Tan, Peng Wang, Guangli Li
{"title":"将DeepSeek的人工智能创新与脑机接口协同起来","authors":"Canbiao Wu, Nayu Chen, Tuo Sun, Ping Tan, Peng Wang, Guangli Li","doi":"10.1002/brx2.70035","DOIUrl":null,"url":null,"abstract":"<p>The integration of artificial intelligence (AI) and brain–computer interfaces (BCIs) represents a significant advancement in neurotechnology, with broad potential applications in healthcare, communication, and human augmentation. This study examines the synergy between DeepSeek, a leader in efficient, open-source AI models, and next-generation BCI technologies. We analyze DeepSeek's contributions to model training efficiency, adaptive reasoning, and open-source accessibility, and propose a framework for BCI development that incorporates these innovations. Additionally, we explore how AI-driven neural signal processing, hardware optimization, and ethical AI–BCI systems can address the critical limitations of current BCI technologies, including signal fidelity, scalability, and real-world applicability. Finally, we offer recommendations for interdisciplinary collaboration, regulatory improvements, and equitable technology dissemination to foster the sustainable development of AI–BCI technology.</p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"3 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.70035","citationCount":"0","resultStr":"{\"title\":\"Synergizing DeepSeek's artificial intelligence innovations with brain–computer interfaces\",\"authors\":\"Canbiao Wu, Nayu Chen, Tuo Sun, Ping Tan, Peng Wang, Guangli Li\",\"doi\":\"10.1002/brx2.70035\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The integration of artificial intelligence (AI) and brain–computer interfaces (BCIs) represents a significant advancement in neurotechnology, with broad potential applications in healthcare, communication, and human augmentation. This study examines the synergy between DeepSeek, a leader in efficient, open-source AI models, and next-generation BCI technologies. We analyze DeepSeek's contributions to model training efficiency, adaptive reasoning, and open-source accessibility, and propose a framework for BCI development that incorporates these innovations. Additionally, we explore how AI-driven neural signal processing, hardware optimization, and ethical AI–BCI systems can address the critical limitations of current BCI technologies, including signal fidelity, scalability, and real-world applicability. Finally, we offer recommendations for interdisciplinary collaboration, regulatory improvements, and equitable technology dissemination to foster the sustainable development of AI–BCI technology.</p>\",\"PeriodicalId\":94303,\"journal\":{\"name\":\"Brain-X\",\"volume\":\"3 2\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.70035\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Brain-X\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/brx2.70035\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brain-X","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/brx2.70035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Synergizing DeepSeek's artificial intelligence innovations with brain–computer interfaces
The integration of artificial intelligence (AI) and brain–computer interfaces (BCIs) represents a significant advancement in neurotechnology, with broad potential applications in healthcare, communication, and human augmentation. This study examines the synergy between DeepSeek, a leader in efficient, open-source AI models, and next-generation BCI technologies. We analyze DeepSeek's contributions to model training efficiency, adaptive reasoning, and open-source accessibility, and propose a framework for BCI development that incorporates these innovations. Additionally, we explore how AI-driven neural signal processing, hardware optimization, and ethical AI–BCI systems can address the critical limitations of current BCI technologies, including signal fidelity, scalability, and real-world applicability. Finally, we offer recommendations for interdisciplinary collaboration, regulatory improvements, and equitable technology dissemination to foster the sustainable development of AI–BCI technology.