Validated on the BNCI2014001, BNCI2014002 and BNCI2015001 datasets, the accuracy rates were 78.78%, 82.11% and 78.19% respectively, with average accuracy outperforming baseline algorithms by 0.13% to 27.7%, and the authors say the method is robust to subject variability. PMID 41941946, DOI 10.1016/j.jneumeth.2026.110768
Dynamic Source Domain Selection: An Adaptive EEG Transfer Learning Framework
Summary
To reduce negative transfer in motor imagery BCIs, the study presents an adaptive dynamic transfer learning framework that decomposes EEG time-frequency features via wavelet convolution, matches source and target samples through a dynamic transfer attention module, and uses a joint loss to shrink marginal and class-conditional differences. On BNCI2014001, BNCI2014002 and BNCI2015001 it reached 78.78%, 82.11% and 78.19% accuracy, averaging 0.13% to 27.7% above baseline algorithms.
Why it matters
Selecting source domains dynamically instead of pooling them naively targets negative transfer — the failure mode driven by low source-target similarity and inter-subject variability that undercuts cross-subject MI-BCI transfer.
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pubmed.ncbi.nlm.nih.gov 2026-04-07