Selective Multi-Source Domain Adaptation Network for Cross-Subject Motor
Imagery Discrimination
Abstract
Discriminating motor imagery with electroencephalogram (EEG)-based
brain-computer interface (BCI) poses a challenge as it involves an
extensive data acquisition phase that demands a substantial amount of
effort from the user. To address this issue, one approach is to use
unsupervised domain adaptation, where classification models are
constructed using data from multiple subjects, and only the unlabeled
data from the target user is used for model calibration. However, since
brain patterns from motor imagery vary between individuals, the
reliability of each subject must be considered when multiple subjects
are used to build the classification model. Thus in this paper, we
propose Selective-MDA that performs domain adaptation on each source
subject and selectively limits influences based on their domain
discrepancies. To evaluate our approach, we assess our results with two
public datasets, BCI Competition IV IIa and the Autocalibration and
Recurrent Adaptation datasets. We further investigate the effect of
source selection by comparing the discrimination performance when
different numbers of source domains are selected based on discrepancy
measures. Our results demonstrate that Selective-MDA not only integrates
multi-source domain adaptation to cross-subject motor imagery
discrimination but also highlights the impact of source domain selection
when using data from multiple subjects for model training.