Integrating additional modalities, such as eye-tracking or behavioral data, can significantly enhance the reconstruction capabilities of the proposed framework by providing complementary information that enriches the understanding of neural responses and visual processing. Eye-tracking data, for example, can offer insights into where and how individuals direct their attention during visual tasks, allowing the framework to align neural activity with specific visual stimuli more accurately. By incorporating eye-tracking information into the training process, the GAN model can learn to reconstruct visual images based on both neural signals and gaze patterns, leading to more precise and contextually relevant reconstructions.
Behavioral data, including task performance metrics or cognitive assessments, can provide valuable context for interpreting neural activity patterns and their relationship to visual stimuli. By integrating behavioral data into the reconstruction framework, researchers can create a more comprehensive model of the brain's response to visual inputs, taking into account individual differences in cognitive processing and task engagement. This holistic approach can improve the accuracy and interpretability of the reconstructed visual images, enabling a deeper understanding of the mechanisms underlying visual encoding and decoding processes in the human brain.