Masked Generative-Contrastive Representation Learning for Cross-Dataset EEG-Based Emotion Recognition

• URL: http://arxiv.org/abs/2607.04139v1
• Date: Sun, 05 Jul 2026 06:44:17 GMT
• Title: Masked Generative-Contrastive Representation Learning for Cross-Dataset EEG-Based Emotion Recognition
• Abstract summary: Masked Generative-Contrastive Learning (MGCRL) is a novel SSL framework specifically designed for EEG-based emotion recognition.<n>It integrates generative and contrastive learning to achieve both fine-grained and global discnative representations for cross-dataset generalization.<n>Extensive experiments involving pretraining on the large FACED dataset and fine-tuning on multiple SEED-series datasets demonstrate MGCRL's effectiveness.
• Score: 14.865111252973556
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised learning (SSL) shows strong potential for cross-dataset transfer by improving feature representation and generalization. However, its application to EEG-based emotion recognition remains largely unexplored. Existing SSL methods struggle to capture the intricate spatiotemporal dependencies of EEG signals under varying channel configurations, extract fine-grained representations resilient to noise, and derive global features that generalize well across subjects. To address these challenges, we propose Masked Generative-Contrastive Representation Learning (MGCRL), a novel SSL framework specifically designed for EEG-based emotion recognition. Built upon a region-aware spatiotemporal encoder, MGCRL integrates generative and contrastive learning to achieve both fine-grained and global discriminative representations for cross-dataset generalization. MGCRL introduces three key designs: 1) a spatiotemporal encoder that incorporates region-based graph convolution to capture localized spatial and functional relationships, enhancing region-specific feature learning and mitigating the impact of varying EEG channel configurations across datasets; 2) a generative learning mechanism based on the joint embedding predictive architecture (JEPA) that utilizes masked features to capture noise robustness fine-grained representations, improving the model's capability to characterize subtle emotional states; and 3) a contrastive learning strategy that leverages masked and original features to learn temporally stable and cross-subject-invariant representations across the same stimuli, boosting emotion discrimination and cross-subject generalization. Under these designs, MGCRL exhibits remarkable ability to learn universal representation. Extensive experiments involving pretraining on the large FACED dataset and fine-tuning on multiple SEED-series datasets demonstrate the effectiveness of MGCRL.

Related papers

• DARE-EEG: A Foundation Model for Mining Dual-Aligned Representation of EEG [25.4837936625773]
  We propose DARE-EEG, a self-supervised foundation model that enforces the mask-invariance property through dual-aligned representation learning.<n>Specifically, we introduce mask alignment that constrains representations from multiple masked views of the same EEG sample.<n>We also propose conv-linear-probing, a parameter-efficient strategy that adapts pre-trained representations to heterogeneous electrode configurations.
  arXiv  Detail & Related papers  (2026-05-18T12:18:43Z)
• Bidirectional Learning of Facial Action Units and Expressions via Structured Semantic Mapping across Heterogeneous Datasets [85.74213192818668]
  We propose a Structured Semantic Mapping (SSM) framework for bidirectional AU--FE learning under different data domains and heterogeneous supervision.<n>SSM consists of three key components: (1) a shared visual backbone that learns unified facial representations from dynamic AU and FE videos; (2) semantic mediation via a Textual Semantic Prototype (TSP) module; and (3) a Dynamic Prior Mapping (DPM) module that incorporates prior knowledge derived from the Facial Action Coding System.
  arXiv  Detail & Related papers  (2026-04-12T09:08:32Z)
• A General Framework for Generative Self-supervised Learning in Non-invasive Estimation of Physiological Parameters Using Photoplethysmography [3.154286239027942]
  A generative SSRL framework is proposed to utilize the temporal, spectrogram, and temporal-spectrogram mixed domains to explore and incorporate the unique features of PPG for universal and noninvasive physiological parameter estimation.<n>Experiments on physiological parameter estimation tasks showed that the joint performance of CTFGA and DPT outperforms standard generative learning significantly.
  arXiv  Detail & Related papers  (2026-04-03T12:42:55Z)
• Improving Deepfake Detection with Reinforcement Learning-Based Adaptive Data Augmentation [60.04281435591454]
  CRDA (Curriculum Reinforcement-Learning Data Augmentation) is a novel framework guiding detectors to progressively master multi-domain forgery features.<n>Central to our approach is integrating reinforcement learning and causal inference.<n>Our method significantly improves detector generalizability, outperforming SOTA methods across multiple cross-domain datasets.
  arXiv  Detail & Related papers  (2025-11-10T12:45:52Z)
• CRIA: A Cross-View Interaction and Instance-Adapted Pre-training Framework for Generalizable EEG Representations [52.251569042852815]
  CRIA is an adaptive framework that utilizes variable-length and variable-channel coding to achieve a unified representation of EEG data across different datasets.<n>The model employs a cross-attention mechanism to fuse temporal, spectral, and spatial features effectively.<n> Experimental results on the Temple University EEG corpus and the CHB-MIT dataset show that CRIA outperforms existing methods with the same pre-training conditions.
  arXiv  Detail & Related papers  (2025-06-19T06:31:08Z)
• CodeBrain: Towards Decoupled Interpretability and Multi-Scale Architecture for EEG Foundation Model [52.466542039411515]
  EEG foundation models (EFMs) have emerged to address the scalability issues of task-specific models.<n>We present CodeBrain, a two-stage EFM designed to fill this gap.<n>In the first stage, we introduce the TFDual-Tokenizer, which decouples heterogeneous temporal and frequency EEG signals into discrete tokens.<n>In the second stage, we propose the multi-scale EEGSSM architecture, which combines structured global convolution with sliding window attention.
  arXiv  Detail & Related papers  (2025-06-10T17:20:39Z)
• An Efficient Self-Supervised Framework for Long-Sequence EEG Modeling [2.1232375739287006]
  We propose EEGM2, a self-supervised framework for EEG representation learning.<n>EEGM2 achieves state-of-the-art performance in both short- and long-sequence modeling and classification.
  arXiv  Detail & Related papers  (2025-02-25T05:57:56Z)
• Training Strategies for Isolated Sign Language Recognition [72.27323884094953]
  This paper introduces a comprehensive model training pipeline for Isolated Sign Language Recognition.<n>The constructed pipeline incorporates carefully selected image and video augmentations to tackle the challenges of low data quality and varying sign speeds.
  arXiv  Detail & Related papers  (2024-12-16T08:37:58Z)
• Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation [56.264157127549446]
  Speech emotion recognition (SER) is a challenging task that plays a crucial role in natural human-computer interaction. One of the main challenges in SER is data scarcity. We propose a transfer learning strategy combined with spectrogram augmentation.
  arXiv  Detail & Related papers  (2021-08-05T10:39:39Z)
• Subject Independent Emotion Recognition using EEG Signals Employing Attention Driven Neural Networks [2.76240219662896]
  A novel deep learning framework capable of doing subject-independent emotion recognition is presented. A convolutional neural network (CNN) with attention framework is presented for performing the task. The proposed approach has been validated using publicly available datasets.
  arXiv  Detail & Related papers  (2021-06-07T09:41:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.