###### Abstract  
This study presents a novel approach using Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to generate synthetic electroencephalography (EEG) and electrocardiogram (ECG) waveforms. The synthetic EEG data represent concentration and relaxation mental states, while the synthetic ECG data correspond to normal and abnormal states. By addressing the challenges of limited biophysical data, including privacy concerns and restricted volunteer availability, our model generates realistic synthetic waveforms learned from real data. Combining real and synthetic datasets improved classification accuracy from 92% to 98.45%, highlighting the benefits of dataset augmentation for machine learning performance. The WGAN-GP model achieved 96.84% classification accuracy for synthetic EEG data representing relaxation states and optimal accuracy for concentration states when classified using a fusion of convolutional neural networks (CNNs). A 50% combination of synthetic and real EEG data yielded the highest accuracy of 98.48%. For EEG signals, the real dataset consisted of 60-s recordings across four channels (TP9, AF7, AF8, and TP10) from four individuals, providing approximately 15,000 data points per subject per state. For ECG signals, the dataset contained 1200 real samples, each comprising 140 data points, representing normal and abnormal states. WGAN-GP outperformed a basic generative adversarial network (GAN) in generating reliable synthetic data. For ECG data, a support vector machine (SVM) classifier achieved an accuracy of 98% with real data and 95.8% with synthetic data. Synthetic ECG data improved the random forest (RF) classifier's accuracy from 97% with real data alone to 98.40% when combined with synthetic data. Statistical significance was assessed using the Wilcoxon signed-rank test, demonstrating the robustness of the WGAN-GP model. Techniques such as discrete wavelet transform, downsampling, and upsampling were employed to enhance data quality. This method shows significant potential in addressing biophysical data scarcity and advancing applications in assistive technologies, human-robot interaction, and mental health monitoring, among other medical applications. s +
Footnote †: journal: Applied Sciences  
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Footnote 1: [[https://doi.org/10.3390/app142310818](https://doi.org/10.3390/app142310818)]([https://doi.org/10.3390/app142310818](https://doi.org/10.3390/app142310818))  
Footnote †: journal: Applied Sciences  
without relying on large, real-world datasets. This can expedite the development of healthcare technologies and improve machine learning models for more accurate predictions in cardiac-related applications. According to [PERSON] et al. [3], the development of GANs has provided a powerful tool for generating synthetic data that closely resemble real data. Deep learning methods have shown significant potential to enhance decoding performance, but their effectiveness often hinges on the availability of large datasets. The generation of synthetic data addresses several critical challenges in this domain. Traditional EEG data acquisition is labor-intensive and requires extensive subject participation and careful calibration to ensure high-quality data, as noted by [4]. [PERSON] et al. [5] emphasize that, while large datasets are crucial for training machine learning models, collecting high-quality EEG data is particularly challenging due to its subject- and session-dependent nature, which necessitates precise calibrations. Similarly, [PERSON] et al. [6] highlight that the accurate identification of motor imagery (MI) patterns in EEG signals is constrained by data-related limitations, hindering the practical implementation of such systems. Furthermore, [PERSON] et al. [7] point out that, while BCIs offer non-invasive communication methods, their efficiency is heavily dependent on individual training data, often acquired during lengthy calibration sessions.