  The inter/intra-subject variability of electroencephalography (EEG) makes the
practical use of the brain-computer interface (BCI) difficult. In general, the
BCI system requires a calibration procedure to tune the model every time the
system is used. This problem is recognized as a major obstacle to BCI, and to
overcome it, approaches based on transfer learning (TL) have recently emerged.
However, many BCI paradigms are limited in that they consist of a structure
that shows labels first and then measures "imagery", the negative effects of
source subjects containing data that do not contain control signals have been
ignored in many cases of the subject-to-subject TL process. The main purpose of
this paper is to propose a method of excluding subjects that are expected to
have a negative impact on subject-to-subject TL training, which generally uses
data from as many subjects as possible. In this paper, we proposed a BCI
framework using only high-confidence subjects for TL training. In our
framework, a deep neural network selects useful subjects for the TL process and
excludes noisy subjects, using a co-teaching algorithm based on the small-loss
trick. We experimented with leave-one-subject-out validation on two public
datasets (2020 international BCI competition track 4 and OpenBMI dataset). Our
experimental results showed that confidence-aware TL, which selects subjects
with small loss instances, improves the generalization performance of BCI.
