Source Data Selection for Brain–Computer Interfaces Based on Simple Features
Carefully selecting the source data is crucial to achieve high performance of transfer learning methods for brain–computer interfaces (BCIs). Especially so in settings where a large amount of source data is available, and finding the optimal source is not computationally feasible. This paper presents a novel method for source selection, the so-called Transfer Performance Predictor (TPP) method. Th
