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 similar to real samples but distinct by augmenting noise to a conditional denoising diffusion probabilistic model (DDPM), thus addressing the prevalent issue of data scarcity in EEG research. The proposed method is tested on the database for emotion analysis using physiological signals (DEAP) and sustained-attention driving task (SADT) datasets, showcasing up to 5.6% improvement in classification accuracy when using synthetic data with DEAP and similar positive results with SADT. This is higher compared to the traditional generative adversarial network based and DDPM based approaches. This study further evaluates the effectiveness of state-of-the-art classifiers on EEG data, employing both real and synthetic data with varying noise levels, and utilizes t-distributed stochastic neighbor embedding and Shapley additive explanations for detailed analysis and interpretability. 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.