## 2 Related Work  
Introduced by [PERSON] et al. [12] in 2014, GANs have become a significant generative technique. [PERSON] et al. [13] highlight the rising interest in using GANs for EEG data generation due to their success in mimicking the temporal, spectral, and spatial features of authentic EEG signals. Since their inception, various GAN types have been developed to overcome initial limitations. Conditional GANs (cGANs) were introduced by [PERSON] and [PERSON] in 2014 [14]. Deep convolutional GANs (DCGANs) were early GAN adaptations that improved training by using deep convolutional neural networks (CNN) for the discriminator and generator [15]. [PERSON] et al. [16] introduced Wasserstein GAN (WGAN). [PERSON] et al. [17] further presented the WGAN-GP as a solution to WGAN restrictions. WGAN-GP could provide more stable training, preventing issues like mode collapse and leading to better overall performance of the GAN. Despite advancements in GAN architectures, several challenges persist, particularly in the context of EEG data generation. [PERSON] et al. [18] reviewed the application of GANs across various EEG domains, including motor imagery, P300, rapid serial visual presentation (RSVP), emotion recognition, and epilepsy. However, limited attention has been given to generating EEG signals related to specific mental states, such as concentration and relaxation. The review also highlighted the challenges in creating diverse synthetic data that adequately capture the complexity of EEG signals. [PERSON] et al. [19] introduced the \"SleepEGAN\" model, which demonstrated the potential of GANs in generating minority class samples for sleep-stage classification. However, its architecture was specifically designed for sleep studies, limiting its generalizability to other EEG applications. This underscores a critical gap in developing GAN models capable of generalizing across different mental states and EEG applications. [PERSON] et al. [20] explored GAN-based data augmentation and anonymization in medical imaging. While their approach showed promise in the medical imaging domain, its application to EEG data generation remains underexplored. [PERSON] and [PERSON]'s \"SynSigGAN\" model [21] generates four types of synthetic biomedical signals, including EEG data related to epilepsy diagnosis and seizure classification. However, their results indicated that the synthetic data were overly similar to the real dataset, limiting its diversity and utility for training machine learning models. [PERSON] et al. [22] introduced the generative adversarial network synthesis for over-sampling (GANSO), a novel method designed to improve classifier training with extremely limited data. GANSO integrates vector Markov random fields (vMRF) with GANs to synthesize data while preserving the structural properties of the original dataset. Its generative block uses graph Fourier transform to maintain graph connectivity, while the discriminative block ensures that the synthetic data are indistinguishable from the original. GANSO demonstrates strong potential for biomedical applications, particularly in scenarios with limited training samples. Unlike GANSO, WGAN-CG operates in the feature space of time-series data, such as ECG and EEG, without explicitly modeling graph dependencies. In this work, we focus on feature-space GANs for time-series data, with plans for future research to incorporate graph-based models. This will enable a direct comparison between feature-space and graph-based methods, such as GANSO, to evaluate their respective efficiencies. [PERSON] et al. [23] demonstrated that WGAN-GP could generate precise and varied EEG signals with improved spectral performance, aiding dataset expansion for traditionally hard-to-collect data. However, their work focused solely on P300 brain waves, with a limited analysis of real-world applicability and signal effectiveness across diverse contexts.