  This study introduces a novel Supervised Info-enhanced Contrastive Learning
framework for EEG based Emotion Recognition (SICLEER). SI-CLEER employs
multi-granularity contrastive learning to create robust EEG contextual
representations, potentiallyn improving emotion recognition effectiveness.
Unlike existing methods solely guided by classification loss, we propose a
joint learning model combining self-supervised contrastive learning loss and
supervised classification loss. This model optimizes both loss functions,
capturing subtle EEG signal differences specific to emotion detection.
Extensive experiments demonstrate SI-CLEER's robustness and superior accuracy
on the SEED dataset compared to state-of-the-art methods. Furthermore, we
analyze electrode performance, highlighting the significance of central frontal
and temporal brain region EEGs in emotion detection. This study offers an
universally applicable approach with potential benefits for diverse EEG
classification tasks.
