Robust Latent Multi-Source Adaptation for Encephalogram-Based Emotion Recognition - Tao, Jianwen; Dan, Yufang; Zhou, Di; He, Songsong (Frontiers in Neuroscience, 2022)
In practical encephalogram (EEG) based machine learning, different subjects can be represented by many different EEG patterns, which would in some extent degrade the performance of extant subject-independent classifiers obtained from cross-subjects datasets. To this end, in this paper, we present a robust Latent Multi-source Adaptation (LMA) framework for cross-subject/dataset emotion recognition with EEG signals by uncovering multiple domain-invariant latent subspaces. Specifically, by jointly aligning the statistical and semantic distribution discrepancies between each source and target pair, multiple domain-invariant classifiers can be trained collaboratively in a unified framework. This framework can fully utilize the correlated knowledge among multiple sources with a novel low-rank regularization term. Comprehensive experiments on DEAP and SEED datasets demonstrates the superior or comparable performance of LMA with the state-of-the-art in the EEG-based emotion recognition.