LRCOMF: A Learn-Review-Challenge Online Meta Learning Framework for EEG Emotion Recognition with Unlabeled Online Samples

Emotion recognition based on Electroencephalogram (EEG) is of great importance for cognitive psychology, disease therapy, etc. However, most mainstream recognition models are trained using batch learning, which fails to adapt to the dynamic and non-stationary nature of real-time EEG streams. In contrast, online learning can adjust model parameters continuously, but it typically relies on labeled data and a fully trained initial model, which are unavailable in practical scenarios. To tackle this challenge, we propose a novel learn-review-challenge online meta-learning framework (LRCOMF) for unlabeled online EEG learning. This framework incorporates a meta updating module through a multi-task cache and a customized sampling strategy to improve the model's generalization during online learning. A sample judgement module is implemented based on a prototype weight being designed to estimate the confidence of predicted labels during the "learn-review" step. Additionally, a challenge module using a clustering quality metric determines whether low-confidence samples can be reconsidered during the "challenge" phase. The validation employed the DEAP and DREAMER datasets. In comparison to the initial baseline condition, the recognition model exhibited substantial enhancements 3.59%/3.87%/1.98% (p$< $0.001) for arousal, valence, dominance labels in the DEAP dataset. Substantial increases of 3.42%/3.63%/2.93% (p$< $0.001) were also noted in DREAMER's evaluation of arousal/valence/dominance labels. This research is essential for tackling unlabeled online learning in authentic EEG contexts and enhancing the advancement of EEG practical applications.