Emotions are crucial in human life, influencing perceptions, relationships, behaviour, and choices. Emotion recognition using Electroencephalography (EEG) in the Brain-Computer Interface (BCI) domain presents significant challenges, particularly the need for extensive datasets. This study aims to generate synthetic EEG samples that are similar to real samples but are distinct by augmenting noise to a conditional denoising diffusion probabilistic model, thus addressing the prevalent issue of data scarcity in EEG research. The proposed method is tested on the DEAP dataset, showcasing a 1.94% improvement in classification performance when using synthetic data. This is higher compared to the traditional GAN-based and DDPM-based approaches. The proposed diffusion-based approach for EEG data generation appears promising in refining the accuracy of emotion recognition systems and marks a notable contribution to EEG-based emotion recognition. Our research further evaluates the effectiveness of state-of-the-art classifiers on EEG data, employing both real and synthetic data with varying noise levels.

翻译：情感在人类生活中至关重要，影响感知、关系、行为与决策。在脑机接口（BCI）领域，利用脑电图（EEG）进行情感识别面临重大挑战，尤其需要大规模数据集。本研究通过向条件去噪扩散概率模型添加噪声，生成与真实样本相似但具有差异性的合成EEG样本，从而解决EEG研究中普遍存在的数据稀缺问题。所提方法在DEAP数据集上进行测试，使用合成数据后分类性能提升1.94%，优于传统基于GAN和DDPM的方法。提出的基于扩散的EEG数据生成方法有望提升情感识别系统的精度，标志着对基于EEG的情感识别的重要贡献。本研究进一步评估了最新分类器在EEG数据上的有效性，同时采用具有不同噪声水平的真实与合成数据。