SDA-DDA: Semi-supervised Domain Adaptation with Dynamic Distribution Alignment Network For Emotion Recognition Using EEG Signals

Objective: As one branch of human-computerinteraction, affective Brain-Computer Interfaces (aBCI) interpretand utilize electroencephalogram (EEG) signalsto achieve real-time monitoring and recognition of individualemotional states, opening new possibilities foremotion-aware technologies and applications. However,the challenge of individual differences in EEG emotiondata severely constrains the effectiveness and generalizationcapability of existing models. Method: To addressthis crucial issue, we propose a novel transfer learningframework known as Semi-Supervised Domain Adaptationwith Dynamic Distribution Alignment (SDA-DDA). Specifically,we align the marginal and conditional probabilitydistributions of the source and target domains using MaximumMean Discrepancy (MMD) and Conditional MaximumMean Discrepancy (CMMD), respectively. Subsequently, adynamic distribution adaptation algorithm is designed todynamically adjust the differences between these two distributionsduring training. In the semi-supervised domainadaptation module, we introduce a pseudo label confidencefiltering mechanism to optimize the quality of pseudo-labelgeneration and enhance the accuracy of conditional distributiondifference estimation. Result: Extensive experimentsconducted on two benchmark databases (SEED andSEED-IV) validate the reliability and stability of the model.Conclusion: Compared to existing literature, our approachachieves satisfactory results in emotion recognition underdifferent evaluation protocols, including cross-subjectand cross-session. Significance: The algorithm proposedin this study enhances the universality and reliability ofemotion recognition, promoting the development of aBCI technology and personalized applications.

搜集汇总

背景与挑战

背景概述

该数据集专注于基于EEG信号的情感识别，提出了一种半监督域自适应框架SDA-DDA，通过动态分布对齐算法处理个体差异问题，以提升模型泛化能力。实验在SEED和SEED-IV基准数据库上进行，验证了其在跨被试和跨会话场景下的有效性，推动了情感脑机接口技术的发展。