###### Abstract
Another significant challenge in accessing medical data is the heavy restrictions imposed due to its sensitive nature, limiting its availability for research and clinical training purposes. As noted by [PERSON] et al. [8], unique brainwave patterns of individuals can be used for authentication techniques, making EEG data a form of biometric information and, therefore, highly sensitive. [PERSON] et al. [9] explore EEG-based biometric cryptosystems for authentication, further underscoring their sensitivity. Standard de-identification techniques, aimed at facilitating data sharing, are often insufficient to fully protect the privacy of individuals in the dataset, as mentioned by [PERSON] et al. [10]. For ECG data, imbalances are a frequent issue because abnormal cases are relatively rare [11]. Additionally, the use of real patient ECGs is heavily regulated due to privacy concerns. This creates a constant demand for additional ECG data, particularly for training machine learning models in automatic diagnosis, which perform better with balanced datasets.The dependency on human subjects also raises ethical concerns related to privacy and consent. Synthetic EEG data provide a way to circumvent many of these issues, enabling innovative research and practical applications. Furthermore, synthetic data can reduce commercial risks in product development for neurotechnology and BCI applications by offering a reliable and scalable source of training data. It addresses challenges such as insufficient or unreliable data, extended timelines for real-world data collection, and the difficulties of obtaining ethical approval for human participants. By minimizing the need for extensive data collection and navigating regulatory landscapes with anonymized data, synthetic data also significantly reduces costs. The main contribution of this research is the development of a novel approach to generating synthetic EEG and ECG data using WGAN-GP combined with our CNN architecture to improve data classification. The synthetic EEG data closely resemble real data for concentration and relaxation states, while the synthetic ECG data correspond to normal and abnormal states. This method increases the dataset size, enhancing the generalization and performance of machine learning models. The application of WGAN-GP for generating synthetic biophysical data represents a significant advancement in medical data generation and analysis, enabling improved diagnostics and research. By leveraging deep learning techniques, the model produces high-fidelity synthetic waves, which augment real datasets--a crucial step for training machine learning models, as larger and more diverse datasets lead to better performance and generalization. Our approach has demonstrated superior accuracy, with the generated synthetic EEG data being effectively classifiable into concentrated and relaxed states. Additionally, the inclusion of synthetic ECG data has been shown to improve classification accuracy, further highlighting the potential of this approach in biomedical and other research fields that depend on biophysical data-driven applications. The structure of this paper is outlined as follows. Section 2 explores background research, reviewing the relevant literature. In Section 3, we introduce our proposed work and outline its development. Section 4 showcases our experimental results and insights. Finally, Section 5 presents the conclusions drawn from our findings and outlines avenues for future research.