  Fully supervised learning has recently achieved promising performance in
various electroencephalography (EEG) learning tasks by training on large
datasets with ground truth labels. However, labeling EEG data for affective
experiments is challenging, as it can be difficult for participants to
accurately distinguish between similar emotions, resulting in ambiguous
labeling (reporting multiple emotions for one EEG instance). This notion could
cause model performance degradation, as the ground truth is hidden within
multiple candidate labels. To address this issue, Partial Label Learning (PLL)
has been proposed to identify the ground truth from candidate labels during the
training phase, and has shown good performance in the computer vision domain.
However, PLL methods have not yet been adopted for EEG representation learning
or implemented for emotion recognition tasks. In this paper, we adapt and
re-implement six state-of-the-art PLL approaches for emotion recognition from
EEG on a large emotion dataset (SEED-V, containing five emotion classes). We
evaluate the performance of all methods in classical and real-world
experiments. The results show that PLL methods can achieve strong results in
affective computing from EEG and achieve comparable performance to fully
supervised learning. We also investigate the effect of label disambiguation, a
key step in many PLL methods. The results show that in most cases, label
disambiguation would benefit the model when the candidate labels are generated
based on their similarities to the ground truth rather than obeying a uniform
distribution. This finding suggests the potential of using label
disambiguation-based PLL methods for real-world affective tasks. We make the
source code of this paper publicly available at:
https://github.com/guangyizhangbci/PLL-Emotion-EEG.

Computer Vision and Pattern Recognition