Alghamdi Ahmed Mohammed, Ashraf M Usman, Bahaddad Adel A, Almarhabi Khalid Ali, Al Shehri Waleed A, Daraz Amil
Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, 21493, Jeddah, Saudi Arabia.
Department of Computer Science, GC Women University Sialkot, Sialkot, 53310, Pakistan.
Sci Rep. 2025 Aug 3;15(1):28295. doi: 10.1038/s41598-025-13289-5.
Electroencephalography (EEG) signals based emotion brain computer interface (BCI) is a significant field in the domain of affective computing where EEG signals are the cause of reliable and objective applications. Despite these advancements, significant challenges persist, including individual differences in EEG signals across subjects during emotion recognition. To cope this challenge, current study introduces a cutting-edge cross subject contrastive learning (CSCL) scheme for EEG signals representation of brain region. The proposed scheme addresses the generalisation across subjects directly, which is a primary challenge in EEG signals-based emotions recognition. The proposed CSCL scheme captures the complex patterns effectively by employing emotions and stimulus contrastive losses within hyperbolic space. CSCL is designed primarily to learn representations that can effectively distinguish signals originating from different brain regions. Further, we evaluate the significance of our proposed CSCL scheme on five different datasets, including SEED, CEED, FACED and MPED, and obtain 97.70%, 96.26%, 65.98%, and 51.30% respectively. The experimental results show that our proposed CSCL scheme demonstrates strong effectiveness while addressing the challenges related to cross subject variability and label noise in the EEG-based emotion recognition system.
基于脑电图（EEG）信号的情感脑机接口（BCI）是情感计算领域的一个重要领域，其中EEG信号是实现可靠且客观应用的关键因素。尽管取得了这些进展，但仍存在重大挑战，包括在情感识别过程中不同受试者EEG信号的个体差异。为应对这一挑战，当前研究引入了一种用于脑区EEG信号表征的前沿跨受试者对比学习（CSCL）方案。所提出的方案直接解决了跨受试者的泛化问题，这是基于EEG信号的情感识别中的一个主要挑战。所提出的CSCL方案通过在双曲空间中采用情感和刺激对比损失有效地捕捉复杂模式。CSCL主要设计用于学习能够有效区分来自不同脑区信号的表征。此外，我们在包括SEED、CEED、FACED和MPED在内的五个不同数据集上评估了我们提出的CSCL方案的有效性，分别获得了97.70%、96.26%、65.98%和51.30%的准确率。实验结果表明，我们提出的CSCL方案在解决基于EEG的情感识别系统中与跨受试者变异性和标签噪声相关的挑战时表现出强大的有效性。
