Cross-subject electroencephalogram emotion recognition based on maximum classifier discrepancy

情感脑机接口在人机交互领域中具有重要的应用价值。脑电（EEG）由于在时间分辨率、可靠性和准确性方面具备的优势，在情绪识别领域受到广泛关注。然而，脑电的非平稳特性和个体差异限制了情绪识别模型在不同时间、不同受试者之间的泛化。为解决跨被试、跨时间情绪分类的问题，本文提出了最大分类器差异域对抗方法（MCD_ DA），通过建立神经网络情感识别模型，将浅层特征提取器分别对抗域分类器和情感分类器，进而使特征提取器产生域不变表达，在实现近似联合分布适配的同时训练分类器学习任务特异性的决策边界。实验结果表明，在进行跨被试情绪识别时，相较于传统通用分类器 58.23% 的平均分类准确率，该方法的平均分类准确率达到了 88.33%。本研究结果提高了情感脑机接口在实际应用中的泛化能力，为情感脑机接口走向实际应用提供了新的方法。.; Affective brain-computer interfaces (aBCIs) has important application value in the field of human-computer interaction. Electroencephalogram (EEG) has been widely concerned in the field of emotion recognition due to its advantages in time resolution, reliability and accuracy. However, the non-stationary characteristics and individual differences of EEG limit the generalization of emotion recognition model in different time and different subjects. In this paper, in order to realize the recognition of emotional states across different subjects and sessions, we proposed a new domain adaptation method, the maximum classifier difference for domain adversarial neural networks (MCD_DA). By establishing a neural network emotion recognition model, the shallow feature extractor was used to resist the domain classifier and the emotion classifier, respectively, so that the feature extractor could produce domain invariant expression, and train the decision boundary of classifier learning task specificity while realizing approximate joint distribution adaptation. The experimental results showed that the average classification accuracy of this method was 88.33% compared with 58.23% of the traditional general classifier. It improves the generalization ability of emotion brain-computer interface in practical application, and provides a new method for aBCIs to be used in practice.