CLDTA: Contrastive Learning based on Diagonal Transformer Autoencoder for Cross-Dataset EEG Emotion Recognition

TL;DR

This paper introduces CLDTA, a novel contrastive learning framework with a diagonal transformer autoencoder for EEG emotion recognition, capable of generalizing across diverse datasets and adapting to new subjects with minimal data.

Contribution

The paper presents a new deep learning model that enhances cross-dataset EEG emotion recognition and enables rapid subject adaptation through contrastive learning and a diagonal masking strategy.

Findings

Consistent performance across multiple EEG datasets.

Effective in detecting task-specific and general emotional features.

Facilitates rapid adaptation to new subjects with minimal samples.

Abstract

Recent advances in non-invasive EEG technology have broadened its application in emotion recognition, yielding a multitude of related datasets. Yet, deep learning models struggle to generalize across these datasets due to variations in acquisition equipment and emotional stimulus materials. To address the pressing need for a universal model that fluidly accommodates diverse EEG dataset formats and bridges the gap between laboratory and real-world data, we introduce a novel deep learning framework: the Contrastive Learning based Diagonal Transformer Autoencoder (CLDTA), tailored for EEG-based emotion recognition. The CLDTA employs a diagonal masking strategy within its encoder to extracts full-channel EEG data's brain network knowledge, facilitating transferability to the datasets with fewer channels. And an information separation mechanism improves model interpretability by enabling straightforward visualization of brain networks. The CLDTA framework employs contrastive learning to distill subject-independent emotional representations and uses a calibration prediction process to enable rapid adaptation of the model to new subjects with minimal samples, achieving accurate emotion recognition. Our analysis across the SEED, SEED-IV, SEED-V, and DEAP datasets highlights CLDTA's consistent performance and proficiency in detecting both task-specific and general features of EEG signals related to emotions, underscoring its potential to revolutionize emotion recognition research.