Abstract
Affective brain-computer interfaces (aBCIs) are increasingly recognized for their potential in monitoring and interpreting emotional states through electroencephalography (EEG) signals. Current EEG-based emotion recognition methods perform well with short segments of EEG data. However, these methods encounter significant challenges in real-life scenarios where emotional states evolve over extended periods. To address this issue, we propose a Dual Attentive (DuA) transformer framework for long-term continuous EEG emotion analysis. Unlike segment-based approaches, the DuA transformer processes an entire EEG trial as a whole, identifying emotions at the trial level, referred to as trial-based emotion analysis. This framework is designed to adapt to varying signal lengths, providing a substantial advantage over traditional methods. The DuA transformer incorporates three key modules: the spatial-spectral network module, the temporal network module, and the transfer learning module. The spatial-spectral network module simultaneously captures spatial and spectral information from EEG signals, while the temporal network module detects temporal dependencies within long-term EEG data. The transfer learning module enhances the model's adaptability across different subjects and conditions. We extensively evaluate the DuA transformer using a self-constructed long-term EEG emotion database, along with two benchmark EEG emotion databases. On the basis of the trial-based leave-one-subject-out cross-subject cross-validation protocol, our experimental results demonstrate that the proposed DuA transformer significantly outperforms existing methods in long-term continuous EEG emotion analysis, with an average enhancement of 5.28%.
Abstract (translated)
情感脑-计算机接口（aBCIs）越来越被认为是通过电生理学（EEG）信号监测和解释情感状态的潜力。目前基于EEG数据的情感识别方法在短时间EEG数据上表现良好。然而，在现实生活中，情感状态会随着长时间的发展而演变，这些方法遇到了很大的挑战。为了解决这个问题，我们提出了一个双注意（DuA）Transformer框架来进行长期连续EEG情感分析。与基于片段的方法不同，DuA Transformer处理整个EEG试题为一个整体，在试题级别上识别情感，称为试题级情感分析。这个框架旨在适应不同的信号长度，在传统方法中具有很大的优势。DuA Transformer包括三个关键模块：空间-频谱网络模块、时间网络模块和迁移学习模块。空间-频谱网络模块同时捕获EEG信号的空间和频谱信息，而时间网络模块检测长期EEG数据中的时间依赖关系。迁移学习模块提高了模型的适应性，跨不同受试者和条件进行。我们通过自构建的长期EEG情感数据库以及两个基准EEG情感数据库，对DuA Transformer进行了广泛评估。基于试题级跨受试者交叉验证协议，我们的实验结果表明，与现有方法相比，所提出的DuA Transformer在长期连续EEG情感分析中显著表现出优势，平均增益为5.28%。
URL
https://arxiv.org/abs/2407.20519
