  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 upto 4.21% 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.
