One Model for All: Universal Pre-training for EEG based Emotion Recognition across Heterogeneous Datasets and Paradigms

The publication of 'One Model for All: Universal Pre-training for EEG based Emotion Recognition across Heterogeneous Datasets and Paradigms' introduces a novel framework aimed at overcoming the significant challenge of dataset heterogeneity in EEG-based emotion recognition. Traditional models often struggle to generalize due to variability in channels and subjects, leading to poor performance across different datasets. The proposed framework utilizes a two-stage learning process: univariate pre-training through self-supervised contrastive learning and multivariate fine-tuning with advanced architectures like the Adaptive Resampling Transformer and Graph Attention Network. Experimental results indicate that this approach not only stabilizes performance but also achieves state-of-the-art results on benchmarks such as SEED, DEAP, and DREAMER, with notable accuracy gains. The framework's ability to facilitate cross-dataset transfer further underscores its potential impact on the field, mar…