  EEG-based Emotion recognition holds significant promise for applications in
human-computer interaction, medicine, and neuroscience. While deep learning has
shown potential in this field, current approaches usually rely on large-scale
high-quality labeled datasets, limiting the performance of deep learning.
Self-supervised learning offers a solution by automatically generating labels,
but its inter-subject generalizability remains under-explored. For this reason,
our interest lies in offering a self-supervised learning paradigm with better
inter-subject generalizability. Inspired by recent efforts in combining
low-level and high-level tasks in deep learning, we propose a cascaded
self-supervised architecture for EEG emotion recognition. Then, we introduce a
low-level task, time-to-frequency reconstruction (TFR). This task leverages the
inherent time-frequency relationship in EEG signals. Our architecture
integrates it with the high-level contrastive learning modules, performing
self-supervised learning for EEG-based emotion recognition. Experiment on DEAP
and DREAMER datasets demonstrates superior performance of our method over
similar works. The outcome results also highlight the indispensability of the
TFR task and the robustness of our method to label scarcity, validating the
effectiveness of the proposed method.
