## 2 Related Work
Additionally, insufficient data preprocessing resulted in the need for 2000 epochs to achieve satisfactory results, highlighting both computational inefficiency and the necessity for more robust preprocessing techniques to reduce training time and enhance signal quality. The limitations identified in previous studies highlight the need for further research into generating EEG data specific to the mental states of concentration and relaxation. While existing models have shown some success in generating EEG signals, they are often tailored to specific applications (e.g., sleep stages and epilepsy) and fail to produce data that are sufficiently diverse and representative of the complex nature of EEG signals. Moreover, the reliance on specific architectures, such as those used in \"SleepEGAN,\" restricts the versatility of these models. To better understand the classification of brain waves based on their frequency, amplitude, shape, and other characteristics, as noted in [24], it is essential to recognize that these frequencies vary with different mental states, as illustrated in Table 1.  
Evidence from the literature suggests that people with high levels of beta and gamma activity are in a state of concentration, while people with considerable alpha activity are in a state of relaxation. There are cases where the usage of real EEG is considered a privacy breach. [PERSON] et al. [25] mention that brain data can reveal private mental states, necessitating cognitive privacy protections against unauthorized access and collection. In [26], the authors mention that privacy-preserving methods like homomorphic encryption (HE) are limited by high computational demands, noise buildup, and restricted applicability. These limitations further indicate the necessity of generating synthetic data to address privacy concerns effectively. Similar to EEG generation, there are several studies related to synthetic ECG generation. In [27], the authors discuss various approaches to the synthetic generation of ECG using GANs, variational autoencoder-decoders (VAEs), and large language models (LLMs), as well as the limitations of each. The study in [28] focuses primarily on privacy aspects and does not extensively cover the performance or accuracy of the generated synthetic ECGs. In [11], the authors focus on normal cardiac cycles and do not address the generation of abnormal ECG patterns, which are crucial for diagnostic purposes. Apart from GAN-based models, some research has been conducted using GPT models for biophysical data generation. While models like ChatEMG [29] and the GPT-2-based model [30] generate unlimited EMG signal sequences, the proposed WGAN-GP model generates high-quality synthetic data specific to each state, thus achieving higher accuracy in generating classifiable EEG signals and surpassing the fixed sequence limitation. The EEG signals generated via [30] using GPT-2 will be compared to our approach in subsequent sections. This study sought to address these gaps by proposing a novel approach using WGAN-GP for the generation of EEG and ECG signals. The proposed model incorporates efficient data preprocessing techniques, including the use of discrete wavelet transform (DWT) with wavelet Db2 and level 5, to capture both high- and low-frequency components of EEG signals. This approach not only reduces the training time but also enhances the diversity and utility of the synthetic data in training machine learning models. Moreover, this research examines the impact of using real, synthetic, and combined datasets to determine the optimal proportion of synthetic data required to enhance classification accuracy in real-world BCI applications. For EEG signals, we adopted the approach outlined in the research by [PERSON] and [PERSON] [31], which achieved notable success in