The channel-independent design in bioFAME can be extended and scaled to handle high-dimensional multimodal biosignal inputs by incorporating techniques such as hierarchical feature extraction, adaptive channel weighting, and dynamic channel fusion. To handle high-dimensional inputs, researchers can explore hierarchical feature extraction methods that extract features at different levels of abstraction to capture the complex relationships and interactions within the multimodal biosignals. Additionally, adaptive channel weighting mechanisms can be implemented to dynamically adjust the importance of different channels based on the task or input characteristics, allowing the model to focus on relevant information while disregarding noise or irrelevant channels. Furthermore, dynamic channel fusion techniques can be utilized to combine information from multiple channels in a flexible and adaptive manner, enabling the model to effectively integrate and utilize information from diverse modalities.
To maintain the model's robustness and performance, researchers can also incorporate regularization techniques, such as dropout and batch normalization, to prevent overfitting and improve generalization. Additionally, model ensembling and transfer learning strategies can be employed to leverage pre-trained models and enhance the model's performance on high-dimensional multimodal biosignal inputs. By carefully designing the architecture, optimizing the hyperparameters, and incorporating advanced techniques for handling high-dimensional data, bioFAME can be extended and scaled to effectively process and analyze complex multimodal biosignal inputs while maintaining robustness and performance across different tasks and modalities.