Using synthetic motion data for training and evaluating the SVR model may have limitations in capturing the full complexity and variability of real-world motion patterns observed in fMRI scans. Synthetic motion data may not fully represent the diversity of motion artifacts and distortions present in actual clinical or research fMRI datasets. Additionally, the synthetic motion parameters used for generating the data may not perfectly mimic the nuances of real patient motion.
To address these limitations and incorporate real-world motion data, one approach is to collect fMRI scans from actual patients or participants exhibiting a range of motion patterns. These scans can then be annotated with ground truth motion parameters, either through manual labeling or automated motion tracking algorithms. By using real-world data, the SVR model can learn from the variability and complexity of motion artifacts encountered in practice.
Furthermore, data augmentation techniques can be applied to introduce variability in the training data, simulating different motion scenarios and enhancing the model's robustness to unseen motion patterns. Augmentation methods such as random rotations, translations, and deformations can help the model generalize better to diverse motion patterns present in real fMRI scans.