GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition
Previous electroencephalogram (EEG) emotion recognition relies on single-task learning, which may lead to overfitting and learned emotion features lacking generalization. In this paper, a graph-based multi-task self-supervised learning model (GMSS) for EEG emotion recognition is proposed. GMSS has the ability to learn more general representations by integrating multiple self-supervised tasks, including spatial and frequency jigsaw puzzle tasks, and contrastive learning tasks. By learning from multiple tasks simultaneously, GMSS can find a representation that captures all of the tasks thereby decreasing the chance of overfitting on the original task, i.e., emotion recognition task. In particular, the spatial jigsaw puzzle task aims to capture the intrinsic spatial relationships of different brain regions. Considering the importance of frequency information in EEG emotional signals, the goal of the frequency jigsaw puzzle task is to explore the crucial frequency bands for EEG emotion recognition. To further regularize the learned features and encourage the network to learn inherent representations, contrastive learning task is adopted in this work by mapping the transformed data into a common feature space. The performance of the proposed GMSS is compared with several popular unsupervised and supervised methods. Experiments on SEED, SEED-IV, and MPED datasets show that the proposed model has remarkable advantages in learning more discriminative and general features for EEG emotional signals.
Code (1)
Tasks
Contrastive LearningEEGEEG Emotion RecognitionElectroencephalogram (EEG)Emotion RecognitionSelf-Supervised LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent
We consider a natural model of online preference aggregation, where sets of preferred items $R_1, R_2, \ldots, R_t$ along with a demand for $k_t$ items in each $R_t$, appear online. Without prior knowledge of $(R_t, k_t)…
Dimensionality ReductionExploring Correlations of Self-Supervised Tasks for Graphs
Graph self-supervised learning has sparked a research surge in training informative representations without accessing any labeled data. However, our understanding of graph self-supervised learning remains limited, and th…
Multi-Task LearningSelf-Supervised LearningGraph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks
Self-supervised learning is currently gaining a lot of attention, as it allows neural networks to learn robust representations from large quantities of unlabeled data. Additionally, multi-task learning can further improv…
Auxiliary LearningGraph ClassificationMulti-Task LearningRepresentation Learning+1Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels
Graph Convolutional Networks(GCNs) play a crucial role in graph learning tasks, however, learning graph embedding with few supervised signals is still a difficult problem. In this paper, we propose a novel training algor…
Graph EmbeddingGraph LearningSelf-Supervised LearningDistilling Knowledge from Self-Supervised Teacher by Embedding Graph Alignment
Recent advances have indicated the strengths of self-supervised pre-training for improving representation learning on downstream tasks. Existing works often utilize self-supervised pre-trained models by fine-tuning on do…
Knowledge DistillationRepresentation LearningSelf-Supervised Learning