Source Data Selection for Brain–Computer Interfaces Based on Simple Features

Συγγραφείς: Heskebeck, Frida, Author, Bergeling, Carolina, 1990-, Author, Bernhardsson, Bo, Author

Συνεισφορές: Heskebeck, Frida, Author, Bergeling, Carolina, 1990-, Author, Bernhardsson, Bo, Author

Πηγή: eLLIIT – The Linköping – Lund Initiative on IT and Mobile Communication IEEE Access. 14:36191-36201

Θεματικοί όροι: Brain–computer interface, calibration, cross subject, machine learning, Riemannian geometry, source data selection, transfer learning, Data communication systems, Data reduction, Feature Selection, Geometry, Interfaces (computer), Learning systems, Real time control, Data Selection, Machine-learning, Performance predictor, Source data, Source selection, Transfer performance

Περιγραφή αρχείου: electronic

Σύνδεσμος πρόσβασης: https://urn.kb.se/resolve?urn=urn:nbn:se:bth-29271
https://doi.org/10.1109/ACCESS.2026.3670905

Rotation-based metric on the Riemannian manifold of SPD matrices with applications to source data selection for brain-computer interface transfer learning

Συγγραφείς: Heskebeck, Frida, Author, Bernhardsson, Bo, Author, Bergeling, Carolina, 1990-, Author

Συνεισφορές: Heskebeck, Frida, Author, Bernhardsson, Bo, Author, Bergeling, Carolina, 1990-, Author

Πηγή: eLLIIT – The Linköping – Lund Initiative on IT and Mobile Communication Frontiers in Human Neuroscience. 20

Θεματικοί όροι: BCI, pole ratio, Riemann geometry, source data selection, transfer learning

Περιγραφή αρχείου: electronic

Σύνδεσμος πρόσβασης: https://urn.kb.se/resolve?urn=urn:nbn:se:bth-29798
https://doi.org/10.3389/fnhum.2026.1824613

Evaluation of Source Data Selection for DTL Based CSI Feedback Method in FDD Massive MIMO Systems

Συγγραφείς: Inoue, Mayuko, Ohtsuki, Tomoaki, Yamamoto, Kohei, Gui, Guan

Πηγή: 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC) Consumer Communications & Networking Conference (CCNC), 2023 IEEE 20th. :182-187 Jan, 2023

Relation: 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC)

Selecting subsets of source data for transfer learning with applications in metal additive manufacturing

Συγγραφείς: Tang, Yifan^Aff1, Rahmani Dehaghani, Mostafa^Aff1, Sajadi, Pouyan^Aff1, Wang, G. Gary^{Aff1, IDs10845024024026_cor1}

Πηγή: Journal of Intelligent Manufacturing. 36(5):3185-3206

Double-bootstrapping source data selection for instance-based transfer learning

Συγγραφείς: Lin, Di, An, Xing, Zhang, Jian ^⁎

Πηγή: In Pattern Recognition Letters 1 August 2013 34(11):1279-1285

Source Data Selection for Brain–Computer Interfaces Based on Simple Features

Συγγραφείς: Heskebeck, Frida, Bergeling, Carolina, Bernhardsson, Bo

Όροι ευρετηρίου: Brain–computer interface, calibration, cross subject, machine learning, Riemannian geometry, source data selection, transfer learning, Data communication systems, Data reduction, Feature Selection, Geometry, Interfaces (computer), Learning systems, Real time control, Data Selection, Machine-learning, Performance predictor, Source data, Source selection, Transfer performance, Neurosciences, Neurovetenskaper, Computer Sciences, Datavetenskap (datalogi), Article in journal, info:eu-repo/semantics/article, text

Σύνδεσμος: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-29271
IEEE Access, 2026, 14, s. 36191-36201