  Recently, supervised methods, which often require substantial amounts of
class labels, have achieved promising results for EEG representation learning.
However, labeling EEG data is a challenging task. More recently, holistic
semi-supervised learning approaches, which only require few output labels, have
shown promising results in the field of computer vision. These methods,
however, have not yet been adapted for EEG learning. In this paper, we adapt
three state-of-the-art holistic semi-supervised approaches, namely MixMatch,
FixMatch, and AdaMatch, as well as five classical semi-supervised methods for
EEG learning. We perform rigorous experiments with all 8 methods on two public
EEG-based emotion recognition datasets, namely SEED and SEED-IV. The
experiments with different amounts of limited labeled samples show that the
holistic approaches achieve strong results even when only 1 labeled sample is
used per class. Further experiments show that in most cases, AdaMatch is the
most effective method, followed by MixMatch and FixMatch.
