American Psychological Association 6th edition

Chen D, Shi J, Tao B, Zhao X, Zhao Z, Li S, Xu Y, Ding T, Zhang P, Ye Q, Chen K, Wu Z, Tang Y, Jiang W, Shu K, Huang L, You Z, Zhang P, & Tang Z. (2025). A Novel Transfer Learning-Based Hybrid EEG-f NIRS Brain-Computer Interface for Intracerebral Hemorrhage Rehabilitation. Advanced Science (Weinheim, Baden-Wurttemberg, Germany), 12(43), e05426-e05426. https://doi.org/10.1002/advs.202505426

ISO-690 (author-date, English)

CHEN D, SHI J, TAO B, ZHAO X, ZHAO Z, LI S, XU Y, DING T, ZHANG P, YE Q, CHEN K, WU Z, TANG Y, JIANG W, SHU K, HUANG L, YOU Z, ZHANG P und TANG Z, 2025. A Novel Transfer Learning-Based Hybrid EEG-f NIRS Brain-Computer Interface for Intracerebral Hemorrhage Rehabilitation. Advanced science (Weinheim, Baden-Wurttemberg, Germany). 1 November 2025. Vol. 12, no. 43, p. e05426-e05426. DOI 10.1002/advs.202505426.

Modern Language Association 9th edition

Chen D, Shi J, Tao B, Zhao X, Zhao Z, Li S, Xu Y, Ding T, Zhang P, Ye Q, Chen K, Wu Z, Tang Y, Jiang W, Shu K, Huang L, You Z, Zhang P, und Tang Z. „A Novel Transfer Learning-Based Hybrid EEG-FNIRS Brain-Computer Interface for Intracerebral Hemorrhage Rehabilitation.“. Advanced Science (Weinheim, Baden-Wurttemberg, Germany), Bd. 12, Nr. 43, November 2025, S. e05426-e05426, https://doi.org/10.1002/advs.202505426.

Mohr Siebeck - Recht (Deutsch - Österreich)

Chen D/Shi J/Tao B/Zhao X/Zhao Z/Li S u. a.: A Novel Transfer Learning-Based Hybrid EEG-f NIRS Brain-Computer Interface for Intracerebral Hemorrhage Rehabilitation., Advanced science (Weinheim, Baden-Wurttemberg, Germany) 2025, e05426-e05426.

Emerald - Harvard

Chen D, Shi J, Tao B, Zhao X, Zhao Z, Li S, Xu Y, Ding T, Zhang P, Ye Q, Chen K, Wu Z, Tang Y, Jiang W, Shu K, Huang L, You Z, Zhang P und Tang Z. (2025), „A Novel Transfer Learning-Based Hybrid EEG-f NIRS Brain-Computer Interface for Intracerebral Hemorrhage Rehabilitation.“, Advanced Science (Weinheim, Baden-Wurttemberg, Germany), Vol. 12 No. 43, S. e05426-e05426.

Achtung: Diese Zitate sind unter Umständen nicht zu 100% korrekt.