The current training data generation approach using independent component analysis (ICA) to create synthetic noisy-clean EEG pairs has several limitations. One significant limitation is that the generated data may not fully capture the variability and complexity of artifacts encountered in real-world scenarios. For instance, the ICA method relies on the assumption that the observed signals are linear mixtures of independent sources, which may not hold true in all cases, particularly in the presence of non-linear interactions among different artifact types.
To enhance this approach, it would be beneficial to incorporate more sophisticated simulation techniques that account for the non-linear characteristics of EEG signals. For example, using generative adversarial networks (GANs) to create synthetic EEG data that includes a broader range of artifact types and combinations could provide a more realistic training environment. This would allow the model to learn from a richer dataset that reflects the complexities of real-world EEG recordings.
Additionally, collecting real-world EEG data from diverse populations and settings would improve the representativeness of the training data. This could involve conducting experiments in various environments, such as clinical settings, naturalistic contexts, and during different cognitive tasks, to capture a wide array of artifacts. By ensuring that the training data encompasses the full spectrum of potential artifacts, the ART model can be better equipped to generalize its performance to practical applications.