Enhancing transfer performance across datasets for brain-computer interfaces using a combination of alignment strategies and adaptive batch normalization
Objective. Recently, transfer learning (TL) and deep learning (DL) have been introduced to solve intra- and inter-subject variability problems in brain-computer interfaces (BCIs). However, current TL and DL algorithms are usually validated within a single dataset, assuming that data of the test subj...
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| Vydané v: | Journal of neural engineering Ročník 18; číslo 4 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
01.08.2021
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| ISSN: | 1741-2552, 1741-2552 |
| On-line prístup: | Zistit podrobnosti o prístupe |
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